Cleaning Data in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae9c9f60",
   "metadata": {},
   "source": [
    "# Removing characters (eg: 'fiat signs', ',' , '+') and converting strings to intergers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "397d5eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548031b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing characters if a column has a $ sign \n",
    "\n",
    "dataset['column_name'] = dataset['column_name'].str.strip('$') #str specifies the string we want to remove which is '$'\n",
    "\n",
    "#the result of the first code converts the values of the column into decimals. We need them as integers. \n",
    "\n",
    "dataset['column_name'] = dataset['column_name'].astype('int') #astype argument used to convert the values into integers\n",
    "\n",
    "#verifying that the values of the column are integers\n",
    "\n",
    "assert dataset['column_name'].dtype == 'int'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87d8733f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesOrderID</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53636</td>\n",
       "      <td>23135$</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74636</td>\n",
       "      <td>63736$</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   SalesOrderID Revenue  Quantity\n",
       "0         53636  23135$        12\n",
       "1         74636  63736$         2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "sales = pd.read_csv('sales.csv')\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "213015c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesOrderID    int64\n",
       "Revenue         int32\n",
       "Quantity        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing the $ signs from the Revenue column\n",
    "\n",
    "sales.Revenue = sales['Revenue'].str.strip('$')\n",
    "sales.Revenue = sales['Revenue'].astype('int')\n",
    "sales.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "773784a0",
   "metadata": {},
   "source": [
    "# The assert statement\n",
    "\n",
    "The assert statement is used to verify if a desired output equals what the program produces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8106ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert 1+1 == 2\n",
    "\n",
    "#running the program returns nothing since the the statement is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c77d9c50",
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-968c1c7d70ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "assert 1+1 == 3\n",
    "\n",
    "#the program returns an attribution error since the statement is not mathematically correct"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0188014c",
   "metadata": {},
   "source": [
    "# converting numerical data to categorical data\n",
    "\n",
    "Some data can have numeric figures but are not necessarily numerical data. They could be categorical data.\n",
    "\n",
    "Eg: 1-Positive\n",
    "    2-Neutral\n",
    "    3-Negative\n",
    "    \n",
    "    or\n",
    "    \n",
    "    Marriage Status\n",
    "    0-Never Married\n",
    "    1-Married\n",
    "    2-Separated\n",
    "    3-Divorced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c8ff916",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting numerical to categorical data\n",
    "\n",
    "df['column_name'] = df['column_name'].astype('category')\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "371b484d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0          int64\n",
      "duration           object\n",
      "station_A_id        int64\n",
      "station_A_name     object\n",
      "station_B_id        int64\n",
      "station_B_name     object\n",
      "bike_id             int64\n",
      "user_type           int64\n",
      "user_birth_year     int64\n",
      "user_gender        object\n",
      "dtype: object\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25760 entries, 0 to 25759\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Unnamed: 0       25760 non-null  int64 \n",
      " 1   duration         25760 non-null  object\n",
      " 2   station_A_id     25760 non-null  int64 \n",
      " 3   station_A_name   25760 non-null  object\n",
      " 4   station_B_id     25760 non-null  int64 \n",
      " 5   station_B_name   25760 non-null  object\n",
      " 6   bike_id          25760 non-null  int64 \n",
      " 7   user_type        25760 non-null  int64 \n",
      " 8   user_birth_year  25760 non-null  int64 \n",
      " 9   user_gender      25760 non-null  object\n",
      "dtypes: int64(6), object(4)\n",
      "memory usage: 2.0+ MB\n",
      "None\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>duration</th>\n",
       "      <th>station_A_id</th>\n",
       "      <th>station_A_name</th>\n",
       "      <th>station_B_id</th>\n",
       "      <th>station_B_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>user_birth_year</th>\n",
       "      <th>user_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12 minutes</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>323</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>5480</td>\n",
       "      <td>2</td>\n",
       "      <td>1959</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24 minutes</td>\n",
       "      <td>3</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>118</td>\n",
       "      <td>Eureka Valley Recreation Center</td>\n",
       "      <td>5193</td>\n",
       "      <td>2</td>\n",
       "      <td>1965</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>8 minutes</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>23</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>3652</td>\n",
       "      <td>3</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4 minutes</td>\n",
       "      <td>16</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>28</td>\n",
       "      <td>The Embarcadero at Bryant St</td>\n",
       "      <td>1883</td>\n",
       "      <td>1</td>\n",
       "      <td>1979</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>11 minutes</td>\n",
       "      <td>22</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>350</td>\n",
       "      <td>8th St at Brannan St</td>\n",
       "      <td>4626</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    duration  station_A_id  \\\n",
       "0           0  12 minutes            81   \n",
       "1           1  24 minutes             3   \n",
       "2           2   8 minutes            67   \n",
       "3           3   4 minutes            16   \n",
       "4           4  11 minutes            22   \n",
       "\n",
       "                                      station_A_name  station_B_id  \\\n",
       "0                                 Berry St at 4th St           323   \n",
       "1       Powell St BART Station (Market St at 4th St)           118   \n",
       "2  San Francisco Caltrain Station 2  (Townsend St...            23   \n",
       "3                            Steuart St at Market St            28   \n",
       "4                              Howard St at Beale St           350   \n",
       "\n",
       "                    station_B_name  bike_id  user_type  user_birth_year  \\\n",
       "0               Broadway at Kearny     5480          2             1959   \n",
       "1  Eureka Valley Recreation Center     5193          2             1965   \n",
       "2    The Embarcadero at Steuart St     3652          3             1993   \n",
       "3     The Embarcadero at Bryant St     1883          1             1979   \n",
       "4             8th St at Brannan St     4626          2             1994   \n",
       "\n",
       "  user_gender  \n",
       "0        Male  \n",
       "1        Male  \n",
       "2        Male  \n",
       "3        Male  \n",
       "4        Male  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_sharing = pd.read_csv('ride_sharing_new.csv')\n",
    "print(ride_sharing.dtypes)\n",
    "print(ride_sharing.info())\n",
    "ride_sharing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "888ea7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>station_A_id</th>\n",
       "      <th>station_B_id</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>user_birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25760.000000</td>\n",
       "      <td>25760.000000</td>\n",
       "      <td>25760.000000</td>\n",
       "      <td>25760.000000</td>\n",
       "      <td>25760.000000</td>\n",
       "      <td>25760.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12879.500000</td>\n",
       "      <td>31.023602</td>\n",
       "      <td>89.558579</td>\n",
       "      <td>4107.621467</td>\n",
       "      <td>2.008385</td>\n",
       "      <td>1983.054969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7436.415803</td>\n",
       "      <td>26.409263</td>\n",
       "      <td>105.144103</td>\n",
       "      <td>1576.315767</td>\n",
       "      <td>0.704541</td>\n",
       "      <td>10.010992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1901.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6439.750000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3106.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1978.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12879.500000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>4821.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19319.250000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5257.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25759.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>6638.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  station_A_id  station_B_id       bike_id     user_type  \\\n",
       "count  25760.000000  25760.000000  25760.000000  25760.000000  25760.000000   \n",
       "mean   12879.500000     31.023602     89.558579   4107.621467      2.008385   \n",
       "std     7436.415803     26.409263    105.144103   1576.315767      0.704541   \n",
       "min        0.000000      3.000000      3.000000     11.000000      1.000000   \n",
       "25%     6439.750000     15.000000     21.000000   3106.000000      2.000000   \n",
       "50%    12879.500000     21.000000     58.000000   4821.000000      2.000000   \n",
       "75%    19319.250000     67.000000     93.000000   5257.000000      3.000000   \n",
       "max    25759.000000     81.000000    383.000000   6638.000000      3.000000   \n",
       "\n",
       "       user_birth_year  \n",
       "count     25760.000000  \n",
       "mean       1983.054969  \n",
       "std          10.010992  \n",
       "min        1901.000000  \n",
       "25%        1978.000000  \n",
       "50%        1985.000000  \n",
       "75%        1990.000000  \n",
       "max        2001.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_sharing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e64c30ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     25760\n",
       "unique        3\n",
       "top           2\n",
       "freq      12972\n",
       "Name: user_type_cat, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The user_type column contains information on whether a user is taking a free ride and takes on the following values:\n",
    "\n",
    "# 1 for free riders.\n",
    "# 2 for pay per ride.\n",
    "# 3 for monthly subscribers.\n",
    "\n",
    "# Convert user_type from integer to category\n",
    "ride_sharing['user_type_cat'] = ride_sharing['user_type'].astype('category')\n",
    "\n",
    "# Write an assert statement confirming the change\n",
    "assert ride_sharing['user_type_cat'].dtype == 'category'\n",
    "\n",
    "# Print new summary statistics \n",
    "ride_sharing['user_type_cat'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d0cd5157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         duration duration_trim  duration_time\n",
      "0      12 minutes           12              12\n",
      "1      24 minutes           24              24\n",
      "2       8 minutes            8               8\n",
      "3       4 minutes            4               4\n",
      "4      11 minutes           11              11\n",
      "...           ...           ...            ...\n",
      "25755  11 minutes           11              11\n",
      "25756  10 minutes           10              10\n",
      "25757  14 minutes           14              14\n",
      "25758  14 minutes           14              14\n",
      "25759  29 minutes           29              29\n",
      "\n",
      "[25760 rows x 3 columns]\n",
      "11.389052795031056\n"
     ]
    }
   ],
   "source": [
    "# Strip duration of minutes\n",
    "ride_sharing['duration_trim'] = ride_sharing['duration'].str.strip('minutes')\n",
    "\n",
    "# Convert duration to integer\n",
    "ride_sharing['duration_time'] = ride_sharing['duration_trim'].astype('int')\n",
    "\n",
    "# Write an assert statement making sure of conversion\n",
    "assert ride_sharing['duration_time'].dtype == 'int'\n",
    "\n",
    "# Print formed columns and calculate average ride duration \n",
    "print(ride_sharing[['duration','duration_trim','duration_time']])\n",
    "print(ride_sharing['duration_time'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18794610",
   "metadata": {},
   "source": [
    "# Data Range constraints\n",
    "\n",
    "Cleaning data with numeric figures that falls within a range\n",
    "\n",
    "How to deal without range:\n",
    ". Dropping data: only drop data when a small proportion of your dataset is affected by out of range values\n",
    ". Setting custom minimums and maximums\n",
    ". Treat as missing and impute\n",
    ". setting custom value depending on business assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "91ec4124",
   "metadata": {},
   "outputs": [
    {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_name</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Frozen 2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shrek</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Beautiful Mind</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>La Vita</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amelie</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_name  avg_rating\n",
       "0     The Godfather           5\n",
       "1          Frozen 2           3\n",
       "2             Shrek           4\n",
       "3  A Beautiful Mind           6\n",
       "4           La Vita           6\n",
       "5            Amelie           6"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('movies.csv')\n",
    "movies\n",
    "\n",
    "#rating is any integer between 1 - 5\n",
    "#some movies have an average rating exceeding 6 which needs to be fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7428651f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_name</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Frozen 2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shrek</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_name  avg_rating\n",
       "0  The Godfather           5\n",
       "1       Frozen 2           3\n",
       "2          Shrek           4"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping movies with average rating exceeding 5\n",
    "#dropping using values <=5\n",
    "\n",
    "movies = movies[movies['avg_rating'] <=5]\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e9d499df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#alternative for dropping movies with average rating exceeding 5\n",
    "#dropping values using the .drop() method\n",
    "\n",
    "movies.drop(movies[movies['avg_rating'] > 5].index, inplace=True) #inplace=True removes the values inplace so we do not have to create a new column\n",
    "assert movies['avg_rating'].max() <= 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b4170443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_name</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Frozen 2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shrek</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Beautiful Mind</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>La Vita</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Amelie</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_name  avg_rating\n",
       "0     The Godfather           5\n",
       "1          Frozen 2           3\n",
       "2             Shrek           4\n",
       "3  A Beautiful Mind           5\n",
       "4           La Vita           5\n",
       "5            Amelie           5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can set an argument using loc method that if any value exceeds the max, set the value to be equal to the max\n",
    "#convert avg_rating > 5 to 5\n",
    "\n",
    "movies.loc[movies['avg_rating'] > 5, 'avg_rating'] = 5\n",
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b7f822",
   "metadata": {},
   "source": [
    "# Filtering out date data ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6e3383a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "today_date = dt.date.today()\n",
    "\n",
    "#drop dates exceeding current date using filtering method\n",
    "df = df[df['date_column'] < today_date]\n",
    "\n",
    "#using .drop() method to filter out dates in a dataset that exceeds the current date\n",
    "df.drop[df[df['date_column'] > today_date].index, inplace=True]\n",
    "\n",
    "#using loc method to set dates exceeding current date to be equal to current date\n",
    "df.loc[df['date_column'] > today_date, 'date_column'] = today_date\n",
    "\n",
    "#asserting if the date statement is correct\n",
    "df['date_column'].max().date() <= today_date\n",
    "\n",
    "#NB: date_column dates should be organized as yyyy-mm-dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "200cf87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0            int64\n",
       "cust_id              object\n",
       "birth_date           object\n",
       "Age                   int64\n",
       "acct_amount         float64\n",
       "inv_amount            int64\n",
       "fund_A              float64\n",
       "fund_B              float64\n",
       "fund_C              float64\n",
       "fund_D              float64\n",
       "account_opened       object\n",
       "last_transaction     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banking_dirty = pd.read_csv('banking_dirty.csv')\n",
    "banking_dirty.dtypes\n",
    "\n",
    "#the birth_date column is recognised as an object instead of a datetime object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4cad4fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                   int64\n",
       "cust_id                     object\n",
       "birth_date          datetime64[ns]\n",
       "Age                          int64\n",
       "acct_amount                float64\n",
       "inv_amount                   int64\n",
       "fund_A                     float64\n",
       "fund_B                     float64\n",
       "fund_C                     float64\n",
       "fund_D                     float64\n",
       "account_opened              object\n",
       "last_transaction            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting birth_date column to DateTime object\n",
    "\n",
    "banking_dirty['birth_date'] = pd.to_datetime(banking_dirty['birth_date'])\n",
    "banking_dirty.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eacc6fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>cust_id</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>Age</th>\n",
       "      <th>acct_amount</th>\n",
       "      <th>inv_amount</th>\n",
       "      <th>fund_A</th>\n",
       "      <th>fund_B</th>\n",
       "      <th>fund_C</th>\n",
       "      <th>fund_D</th>\n",
       "      <th>account_opened</th>\n",
       "      <th>last_transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>870A9281</td>\n",
       "      <td>1962-06-09</td>\n",
       "      <td>58</td>\n",
       "      <td>63523.31</td>\n",
       "      <td>51295</td>\n",
       "      <td>30105.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>15632.0</td>\n",
       "      <td>02-09-18</td>\n",
       "      <td>22-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>166B05B0</td>\n",
       "      <td>1962-12-16</td>\n",
       "      <td>58</td>\n",
       "      <td>38175.46</td>\n",
       "      <td>15050</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>6696.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>28-02-19</td>\n",
       "      <td>31-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BFC13E88</td>\n",
       "      <td>1990-09-12</td>\n",
       "      <td>34</td>\n",
       "      <td>59863.77</td>\n",
       "      <td>24567</td>\n",
       "      <td>10323.0</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>8469.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>25-04-18</td>\n",
       "      <td>02-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>F2158F66</td>\n",
       "      <td>1985-11-03</td>\n",
       "      <td>35</td>\n",
       "      <td>84132.10</td>\n",
       "      <td>23712</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>6482.0</td>\n",
       "      <td>12830.0</td>\n",
       "      <td>07-11-17</td>\n",
       "      <td>08-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7A73F334</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>30</td>\n",
       "      <td>120512.00</td>\n",
       "      <td>93230</td>\n",
       "      <td>12158.4</td>\n",
       "      <td>51281.0</td>\n",
       "      <td>13434.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>14-05-18</td>\n",
       "      <td>19-07-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   cust_id birth_date  Age  acct_amount  inv_amount   fund_A  \\\n",
       "0           0  870A9281 1962-06-09   58     63523.31       51295  30105.0   \n",
       "1           1  166B05B0 1962-12-16   58     38175.46       15050   4995.0   \n",
       "2           2  BFC13E88 1990-09-12   34     59863.77       24567  10323.0   \n",
       "3           3  F2158F66 1985-11-03   35     84132.10       23712   3908.0   \n",
       "4           4  7A73F334 1990-05-17   30    120512.00       93230  12158.4   \n",
       "\n",
       "    fund_B   fund_C   fund_D account_opened last_transaction  \n",
       "0   4138.0   1420.0  15632.0       02-09-18         22-02-19  \n",
       "1    938.0   6696.0   2421.0       28-02-19         31-10-18  \n",
       "2   4590.0   8469.0   1185.0       25-04-18         02-04-18  \n",
       "3    492.0   6482.0  12830.0       07-11-17         08-11-18  \n",
       "4  51281.0  13434.0  18383.0       14-05-18         19-07-18  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banking_dirty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37d89e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2022, 1, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "today_date = dt.date.today()\n",
    "today_date"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d863cb",
   "metadata": {},
   "source": [
    "# Cleaning Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f1b91547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>cup</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "0  Yum Yum   cup     4.0\n",
       "1  Yum Yum   cup     4.0\n",
       "2  Indomie   cup     3.5\n",
       "3  Indomie  pack    15.0\n",
       "4  Indomie  pack     5.0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample dataframe with duplicates\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],\n",
    "    'style': ['cup', 'cup', 'cup', 'pack', 'pack'],\n",
    "    'rating': [4, 4, 3.5, 15, 5]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "fbaf4b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking which rows have duplicates\n",
    "\n",
    "df_duplicates = df.duplicated()\n",
    "df_duplicates\n",
    "\n",
    "#Row index 1 is a duplicate since the result shows True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c3b760ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "1  Yum Yum   cup     4.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#showing the row with duplicate values\n",
    "\n",
    "df[df_duplicates]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ef718c",
   "metadata": {},
   "source": [
    "# How to find Duplicate Rows\n",
    "\n",
    "Using only the .duplicate() method will not always provide an accurate result of rows with duplicate. Having duplicates in some columns does not mean that the rows are duplicate.\n",
    "\n",
    "A more accurate way to find duplicate rows are as follow:\n",
    "1. The .duplicated() method\n",
    "   Subset: List of column names to check for duplication\n",
    "   Keep: Whether to keep first ('first'), last ('last'), or all (False) duplicate values\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4a2cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#column names to check for duplication\n",
    "\n",
    "column_names = ['col_name1', 'col_name2', 'col_name3']\n",
    "duplicates = df.duplicated(subset = column_names, keep = False)\n",
    "df[duplicates]\n",
    "\n",
    "#sorting the duplicates to get a better eyeview\n",
    "#sorting duplicates by first column name ('col_name1')\n",
    "\n",
    "df[duplicates].sort_values(by = 'col_name1') #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c1a25f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>cup</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "0  Yum Yum   cup     4.0\n",
       "1  Yum Yum   cup     4.0\n",
       "2  Indomie   cup     3.5\n",
       "3  Indomie  pack    15.0\n",
       "4  Indomie  pack     5.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "\n",
    "#There are 2 sets of duplicates in this dataframe: \n",
    "# 1. Duplicates accross all rows in row 0 and 1\n",
    "# 2. Duplicates in row 3 and 4 with descrepancies in the ratings column (15.0 and 5.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "56bbc2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yum Yum</td>\n",
       "      <td>cup</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "0  Yum Yum   cup     4.0\n",
       "1  Yum Yum   cup     4.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['brand', 'style', 'rating']\n",
    "duplicates = df.duplicated(subset = column_names, keep = False)\n",
    "df[duplicates]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbca430c",
   "metadata": {},
   "source": [
    "# drop duplicates\n",
    "\n",
    "1. The .drop_duplicates() method\n",
    "2. The .groupby() and .agg() method: \n",
    "    This is used when there are descrepancies in values of a column but we do not want to drop any of the duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c10356a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>cup</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "2  Indomie   cup     3.5\n",
       "3  Indomie  pack    15.0\n",
       "4  Indomie  pack     5.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The .drop_duplicates() method\n",
    "\n",
    "df.drop_duplicates(inplace = True, keep = False)\n",
    "df\n",
    "\n",
    "#the second duplicate is dropped and we have this dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cfadc2f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "3  Indomie  pack    15.0\n",
       "4  Indomie  pack     5.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['brand', 'style']\n",
    "duplicates = df.duplicated(subset = column_names, keep = False)\n",
    "df[duplicates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "50521959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "4  Indomie  pack     5.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['brand', 'style']\n",
    "duplicates = df.duplicated(subset = column_names, keep = 'first')\n",
    "df[duplicates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "020c7648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "3  Indomie  pack    15.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['brand', 'style']\n",
    "duplicates = df.duplicated(subset = column_names, keep = 'last')\n",
    "df[duplicates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1fa7b386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [brand, style, rating]\n",
      "Index: []\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brand</th>\n",
       "      <th>style</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>cup</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Indomie</td>\n",
       "      <td>pack</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     brand style  rating\n",
       "0  Indomie   cup       3\n",
       "1  Indomie  pack      15"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The .groupby() and .agg() method\n",
    "\n",
    "column_names = ['brand', 'style']\n",
    "duplicates = df.duplicated(subset= column_names, keep=False)\n",
    "print(df[duplicates])\n",
    "summaries = {'rating':'max'}\n",
    "df = df.groupby(by = column_names).agg(summaries).reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b3f03641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand      object\n",
       "style      object\n",
       "rating    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "064f978d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     3\n",
       "3    15\n",
       "4     5\n",
       "Name: rating, dtype: int32"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rating = df.rating.astype(int)\n",
    "df.rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea50e525",
   "metadata": {},
   "source": [
    "# Finding Duplicates (Exercise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "57f8a15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>station_A_id</th>\n",
       "      <th>station_A_name</th>\n",
       "      <th>station_B_id</th>\n",
       "      <th>station_B_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>user_birth_year</th>\n",
       "      <th>user_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12 minutes</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>323</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>5480</td>\n",
       "      <td>2</td>\n",
       "      <td>1959</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24 minutes</td>\n",
       "      <td>3</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>118</td>\n",
       "      <td>Eureka Valley Recreation Center</td>\n",
       "      <td>5193</td>\n",
       "      <td>2</td>\n",
       "      <td>1965</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8 minutes</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>23</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>3652</td>\n",
       "      <td>3</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4 minutes</td>\n",
       "      <td>16</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>28</td>\n",
       "      <td>The Embarcadero at Bryant St</td>\n",
       "      <td>1883</td>\n",
       "      <td>1</td>\n",
       "      <td>1979</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11 minutes</td>\n",
       "      <td>22</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>350</td>\n",
       "      <td>8th St at Brannan St</td>\n",
       "      <td>4626</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration  station_A_id  \\\n",
       "0  12 minutes            81   \n",
       "1  24 minutes             3   \n",
       "2   8 minutes            67   \n",
       "3   4 minutes            16   \n",
       "4  11 minutes            22   \n",
       "\n",
       "                                      station_A_name  station_B_id  \\\n",
       "0                                 Berry St at 4th St           323   \n",
       "1       Powell St BART Station (Market St at 4th St)           118   \n",
       "2  San Francisco Caltrain Station 2  (Townsend St...            23   \n",
       "3                            Steuart St at Market St            28   \n",
       "4                              Howard St at Beale St           350   \n",
       "\n",
       "                    station_B_name  bike_id  user_type  user_birth_year  \\\n",
       "0               Broadway at Kearny     5480          2             1959   \n",
       "1  Eureka Valley Recreation Center     5193          2             1965   \n",
       "2    The Embarcadero at Steuart St     3652          3             1993   \n",
       "3     The Embarcadero at Bryant St     1883          1             1979   \n",
       "4             8th St at Brannan St     4626          2             1994   \n",
       "\n",
       "  user_gender  \n",
       "0        Male  \n",
       "1        Male  \n",
       "2        Male  \n",
       "3        Male  \n",
       "4        Male  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_sharing = pd.read_csv('ride_sharing_new.csv', index_col=0)\n",
    "ride_sharing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "130a878a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>station_A_id</th>\n",
       "      <th>station_A_name</th>\n",
       "      <th>station_B_id</th>\n",
       "      <th>station_B_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>user_birth_year</th>\n",
       "      <th>user_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>9 minutes</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>1225</td>\n",
       "      <td>2</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>9 minutes</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>1225</td>\n",
       "      <td>2</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9696</th>\n",
       "      <td>17 minutes</td>\n",
       "      <td>22</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>102</td>\n",
       "      <td>Irwin St at 8th St</td>\n",
       "      <td>492</td>\n",
       "      <td>3</td>\n",
       "      <td>1961</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15217</th>\n",
       "      <td>17 minutes</td>\n",
       "      <td>22</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>102</td>\n",
       "      <td>Irwin St at 8th St</td>\n",
       "      <td>492</td>\n",
       "      <td>3</td>\n",
       "      <td>1961</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9724</th>\n",
       "      <td>4 minutes</td>\n",
       "      <td>21</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>343</td>\n",
       "      <td>Bryant St at 2nd St</td>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20170</th>\n",
       "      <td>4 minutes</td>\n",
       "      <td>21</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>343</td>\n",
       "      <td>Bryant St at 2nd St</td>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>10 minutes</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>6</td>\n",
       "      <td>The Embarcadero at Sansome St</td>\n",
       "      <td>4442</td>\n",
       "      <td>1</td>\n",
       "      <td>1967</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18303</th>\n",
       "      <td>10 minutes</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>6</td>\n",
       "      <td>The Embarcadero at Sansome St</td>\n",
       "      <td>4442</td>\n",
       "      <td>1</td>\n",
       "      <td>1967</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration  station_A_id  \\\n",
       "566     9 minutes            81   \n",
       "604     9 minutes            81   \n",
       "9696   17 minutes            22   \n",
       "15217  17 minutes            22   \n",
       "9724    4 minutes            21   \n",
       "20170   4 minutes            21   \n",
       "7610   10 minutes            30   \n",
       "18303  10 minutes            30   \n",
       "\n",
       "                                         station_A_name  station_B_id  \\\n",
       "566                                  Berry St at 4th St            81   \n",
       "604                                  Berry St at 4th St            81   \n",
       "9696                              Howard St at Beale St           102   \n",
       "15217                             Howard St at Beale St           102   \n",
       "9724   Montgomery St BART Station (Market St at 2nd St)           343   \n",
       "20170  Montgomery St BART Station (Market St at 2nd St)           343   \n",
       "7610     San Francisco Caltrain (Townsend St at 4th St)             6   \n",
       "18303    San Francisco Caltrain (Townsend St at 4th St)             6   \n",
       "\n",
       "                      station_B_name  bike_id  user_type  user_birth_year  \\\n",
       "566               Berry St at 4th St     1225          2             1993   \n",
       "604               Berry St at 4th St     1225          2             1993   \n",
       "9696              Irwin St at 8th St      492          3             1961   \n",
       "15217             Irwin St at 8th St      492          3             1961   \n",
       "9724             Bryant St at 2nd St     5034          2             1993   \n",
       "20170            Bryant St at 2nd St     5034          2             1993   \n",
       "7610   The Embarcadero at Sansome St     4442          1             1967   \n",
       "18303  The Embarcadero at Sansome St     4442          1             1967   \n",
       "\n",
       "      user_gender  \n",
       "566          Male  \n",
       "604          Male  \n",
       "9696       Female  \n",
       "15217      Female  \n",
       "9724         Male  \n",
       "20170        Male  \n",
       "7610         Male  \n",
       "18303        Male  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ride_duplicates = ride_sharing.duplicated(keep = False)\n",
    "ride_sharing[ride_duplicates].sort_values(by = 'station_A_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11987516",
   "metadata": {},
   "source": [
    "# Categorical and Membership Data Variables\n",
    "\n",
    "1. Anti-Join: a left anti join is used to find rows with inconsistent values \n",
    "2. Inner Join: finding rows with consistent values in multiple datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "490bf185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>birthday</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beth</td>\n",
       "      <td>10/20/2019</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ignatius</td>\n",
       "      <td>7/8/2020</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paul</td>\n",
       "      <td>8/12/2019</td>\n",
       "      <td>0+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helen</td>\n",
       "      <td>3/17/2019</td>\n",
       "      <td>0-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>Z+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kennedy</td>\n",
       "      <td>4/27/2020</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Keith</td>\n",
       "      <td>4/19/2019</td>\n",
       "      <td>AB+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name    birthday blood_type\n",
       "1      Beth  10/20/2019         B-\n",
       "2  Ignatius    7/8/2020         A-\n",
       "3      Paul   8/12/2019         0+\n",
       "4     Helen   3/17/2019         0-\n",
       "5  Jennifer  12/17/2019         Z+\n",
       "6   Kennedy   4/27/2020         A+\n",
       "7     Keith   4/19/2019        AB+"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "study_data = pd.read_csv('study_data.csv', index_col=0)\n",
    "study_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c7fb60c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AB+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AB-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  blood_type\n",
       "1         0-\n",
       "2         0+\n",
       "3         A-\n",
       "4         A+\n",
       "5         B+\n",
       "6         B-\n",
       "7        AB+\n",
       "8        AB-"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = pd.read_csv('categories.csv', index_col=0)\n",
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5ddf728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Z+'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#left anti join\n",
    "inconsistent_categories = set(study_data['blood_type']).difference(categories['blood_type'])\n",
    "inconsistent_categories\n",
    "\n",
    "#the result shows that blood type Z+ does not exist in categories dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76f56b74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4    False\n",
      "5     True\n",
      "6    False\n",
      "7    False\n",
      "Name: blood_type, dtype: bool\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>birthday</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jennifer</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>Z+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name    birthday blood_type\n",
       "5  Jennifer  12/17/2019         Z+"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get and print rows with inconsistent categories\n",
    "\n",
    "inconsistent_rows = study_data['blood_type'].isin(inconsistent_categories)\n",
    "print(inconsistent_rows)\n",
    "\n",
    "study_data[inconsistent_rows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15b0b58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>birthday</th>\n",
       "      <th>blood_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beth</td>\n",
       "      <td>10/20/2019</td>\n",
       "      <td>B-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ignatius</td>\n",
       "      <td>7/8/2020</td>\n",
       "      <td>A-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Paul</td>\n",
       "      <td>8/12/2019</td>\n",
       "      <td>0+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helen</td>\n",
       "      <td>3/17/2019</td>\n",
       "      <td>0-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kennedy</td>\n",
       "      <td>4/27/2020</td>\n",
       "      <td>A+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Keith</td>\n",
       "      <td>4/19/2019</td>\n",
       "      <td>AB+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name    birthday blood_type\n",
       "1      Beth  10/20/2019         B-\n",
       "2  Ignatius    7/8/2020         A-\n",
       "3      Paul   8/12/2019         0+\n",
       "4     Helen   3/17/2019         0-\n",
       "6   Kennedy   4/27/2020         A+\n",
       "7     Keith   4/19/2019        AB+"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping inconsistent rows using the tilde symbol (~)\n",
    "\n",
    "consistent_data = study_data[~inconsistent_rows]\n",
    "consistent_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "533abad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AB-', 'B+'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#right anti join\n",
    "\n",
    "inconsistent_categories = set(categories['blood_type']).difference(study_data['blood_type'])\n",
    "inconsistent_categories\n",
    "\n",
    "#the result shows that blood types 'AB-', 'B+' does not exist in study_data dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a731ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>day</th>\n",
       "      <th>airline</th>\n",
       "      <th>destination</th>\n",
       "      <th>dest_region</th>\n",
       "      <th>dest_size</th>\n",
       "      <th>boarding_area</th>\n",
       "      <th>dept_time</th>\n",
       "      <th>wait_min</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>safety</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1351</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>UNITED INTL</td>\n",
       "      <td>KANSAI</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 91-102</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>373</td>\n",
       "      <td>Friday</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>SAN JOSE DEL CABO</td>\n",
       "      <td>Canada/Mexico</td>\n",
       "      <td>Small</td>\n",
       "      <td>Gates 50-59</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2820</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>DELTA</td>\n",
       "      <td>LOS ANGELES</td>\n",
       "      <td>West US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 40-48</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Somewhat safe</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1157</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>SOUTHWEST</td>\n",
       "      <td>LOS ANGELES</td>\n",
       "      <td>West US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 20-39</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2992</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>East US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 50-59</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>559.0</td>\n",
       "      <td>Somewhat clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>1475</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>NEW YORK-JFK</td>\n",
       "      <td>East US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 50-59</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>280.0</td>\n",
       "      <td>Somewhat clean</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2805</th>\n",
       "      <td>2222</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>SOUTHWEST</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>West US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 20-39</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2806</th>\n",
       "      <td>2684</td>\n",
       "      <td>Friday</td>\n",
       "      <td>UNITED</td>\n",
       "      <td>ORLANDO</td>\n",
       "      <td>East US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 70-90</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>2549</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>JETBLUE</td>\n",
       "      <td>LONG BEACH</td>\n",
       "      <td>West US</td>\n",
       "      <td>Small</td>\n",
       "      <td>Gates 1-12</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Somewhat safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>2162</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>CHINA EASTERN</td>\n",
       "      <td>QINGDAO</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Large</td>\n",
       "      <td>Gates 1-12</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2477 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id        day        airline        destination    dest_region  \\\n",
       "0     1351    Tuesday    UNITED INTL             KANSAI           Asia   \n",
       "1      373     Friday         ALASKA  SAN JOSE DEL CABO  Canada/Mexico   \n",
       "2     2820   Thursday          DELTA        LOS ANGELES        West US   \n",
       "3     1157    Tuesday      SOUTHWEST        LOS ANGELES        West US   \n",
       "4     2992  Wednesday       AMERICAN              MIAMI        East US   \n",
       "...    ...        ...            ...                ...            ...   \n",
       "2804  1475    Tuesday         ALASKA       NEW YORK-JFK        East US   \n",
       "2805  2222   Thursday      SOUTHWEST            PHOENIX        West US   \n",
       "2806  2684     Friday         UNITED            ORLANDO        East US   \n",
       "2807  2549    Tuesday        JETBLUE         LONG BEACH        West US   \n",
       "2808  2162   Saturday  CHINA EASTERN            QINGDAO           Asia   \n",
       "\n",
       "     dest_size boarding_area   dept_time  wait_min     cleanliness  \\\n",
       "0          Hub  Gates 91-102  2018-12-31     115.0           Clean   \n",
       "1        Small   Gates 50-59  2018-12-31     135.0           Clean   \n",
       "2          Hub   Gates 40-48  2018-12-31      70.0         Average   \n",
       "3          Hub   Gates 20-39  2018-12-31     190.0           Clean   \n",
       "4          Hub   Gates 50-59  2018-12-31     559.0  Somewhat clean   \n",
       "...        ...           ...         ...       ...             ...   \n",
       "2804       Hub   Gates 50-59  2018-12-31     280.0  Somewhat clean   \n",
       "2805       Hub   Gates 20-39  2018-12-31     165.0           Clean   \n",
       "2806       Hub   Gates 70-90  2018-12-31      92.0           Clean   \n",
       "2807     Small    Gates 1-12  2018-12-31      95.0           Clean   \n",
       "2808     Large    Gates 1-12  2018-12-31     220.0           Clean   \n",
       "\n",
       "             safety        satisfaction  \n",
       "0           Neutral      Very satisfied  \n",
       "1         Very safe      Very satisfied  \n",
       "2     Somewhat safe             Neutral  \n",
       "3         Very safe  Somewhat satsified  \n",
       "4         Very safe  Somewhat satsified  \n",
       "...             ...                 ...  \n",
       "2804        Neutral  Somewhat satsified  \n",
       "2805      Very safe      Very satisfied  \n",
       "2806      Very safe      Very satisfied  \n",
       "2807  Somewhat safe      Very satisfied  \n",
       "2808      Very safe  Somewhat satsified  \n",
       "\n",
       "[2477 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines = pd.read_csv('airlines_final.csv', index_col=0)\n",
    "airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa904bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>safety</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cleanliness</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Average</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Somewhat clean</td>\n",
       "      <td>Somewhat safe</td>\n",
       "      <td>Somewhat satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Somewhat dirty</td>\n",
       "      <td>very unsafe</td>\n",
       "      <td>Somewhat unsatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dirty</td>\n",
       "      <td>Somewhat unsafe</td>\n",
       "      <td>Very unsatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cleanliness           safety          satisfaction\n",
       "0     cleanliness          Neutral        Very satisfied\n",
       "1         Average        Very safe               Neutral\n",
       "2  Somewhat clean    Somewhat safe    Somewhat satisfied\n",
       "3  Somewhat dirty      very unsafe  Somewhat unsatisfied\n",
       "4           Dirty  Somewhat unsafe      Very unsatisfied"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_airlines = pd.read_csv('categories_airlines.csv', index_col=0)\n",
    "categories_airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "daa17dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleanliness:  ['Clean' 'Average' 'Somewhat clean' 'Somewhat dirty' 'Dirty'] \n",
      "\n",
      "Safety:  ['Neutral' 'Very safe' 'Somewhat safe' 'Very unsafe' 'Somewhat unsafe'] \n",
      "\n",
      "Satisfaction:  ['Very satisfied' 'Neutral' 'Somewhat satsified' 'Somewhat unsatisfied'\n",
      " 'Very unsatisfied'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print unique values of survey columns in airlines\n",
    "print('Cleanliness: ', airlines['cleanliness'].unique(), \"\\n\")\n",
    "print('Safety: ', airlines['safety'].unique(), \"\\n\")\n",
    "print('Satisfaction: ', airlines['satisfaction'].unique(), \"\\n\")\n",
    "\n",
    "#“\\n” is used to create a new line or space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "421e7e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#how to CAPITALIZE column values to make them consistent\n",
    "\n",
    "df['column_name'] = df['column_name'].str.upper()\n",
    "df['column_name'].value_counts()\n",
    "\n",
    "#how to 'lowercase' column values to make them consistent\n",
    "\n",
    "df['column_name'] = df['column_name'].str.lower()\n",
    "df['column_name'].value_counts()\n",
    "\n",
    "#how to to remove trailing and leading spaces (eg: ' married', 'married ')\n",
    "df = df['column_name'].str.strip()\n",
    "df['column_name'].value_counts()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ddb9de",
   "metadata": {},
   "source": [
    "# collapsing data into categories\n",
    "\n",
    "sometimes data could be alot and the best way to easily analyse them is by putting them into groups. For example, household income column can be put into income groups such as 0-10K, 11-20K, etc... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "615ae518",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using qcut()\n",
    "\n",
    "import pandas as pd\n",
    "group_names = ['0-200K', '200k-500k', '500k+']\n",
    "df['income_group'] = pd.qcut(df['household_income'], q=3, labels=group_names)\n",
    "\n",
    "#print income_group column\n",
    "df[['income_group', 'household_income']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fb34c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using cut() - create category ranges and names\n",
    "ranges = [0, 200000, 500000, np.inf]\n",
    "group_names = ['0-200K', '200k-500k', '500k+']\n",
    "\n",
    "#create income group column\n",
    "df['income_group'] = pd.cut(df['household_income'], bins=ranges, labels=group_names)\n",
    "\n",
    "#print income_group column\n",
    "df[['income_group', 'household_income']]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "875917dd",
   "metadata": {},
   "source": [
    "#map categories to fewer ones\n",
    "\n",
    "operating system column is: 'Microsoft', 'MacOS', 'Android', 'IOS', 'Linux'\n",
    "operating system column should be: 'DesktopOS', 'MobileOS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8b388fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create mapping dictionary and replace\n",
    "\n",
    "mapping = {'Microsoft':'DesktopOS', 'MacOS':'DesktopOS', 'Android':'MobileOS', 'IOS':'MobileOS', 'Linux':'DesktopOS'}\n",
    "df['operating_system'] = df['operating_system'].replace(mapping)\n",
    "df['operating_system'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ae60ba",
   "metadata": {},
   "source": [
    "# airlines dataframe practicals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f5345a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>day</th>\n",
       "      <th>airline</th>\n",
       "      <th>destination</th>\n",
       "      <th>dest_region</th>\n",
       "      <th>dest_size</th>\n",
       "      <th>boarding_area</th>\n",
       "      <th>dept_time</th>\n",
       "      <th>wait_min</th>\n",
       "      <th>cleanliness</th>\n",
       "      <th>safety</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1351</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>UNITED INTL</td>\n",
       "      <td>KANSAI</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 91-102</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>373</td>\n",
       "      <td>Friday</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>SAN JOSE DEL CABO</td>\n",
       "      <td>Canada/Mexico</td>\n",
       "      <td>Small</td>\n",
       "      <td>Gates 50-59</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>135.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2820</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>DELTA</td>\n",
       "      <td>LOS ANGELES</td>\n",
       "      <td>West US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 40-48</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Average</td>\n",
       "      <td>Somewhat safe</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1157</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>SOUTHWEST</td>\n",
       "      <td>LOS ANGELES</td>\n",
       "      <td>West US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 20-39</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2992</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>AMERICAN</td>\n",
       "      <td>MIAMI</td>\n",
       "      <td>East US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 50-59</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>559.0</td>\n",
       "      <td>Somewhat clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2804</th>\n",
       "      <td>1475</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>ALASKA</td>\n",
       "      <td>NEW YORK-JFK</td>\n",
       "      <td>East US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 50-59</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>280.0</td>\n",
       "      <td>Somewhat clean</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2805</th>\n",
       "      <td>2222</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>SOUTHWEST</td>\n",
       "      <td>PHOENIX</td>\n",
       "      <td>West US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 20-39</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2806</th>\n",
       "      <td>2684</td>\n",
       "      <td>Friday</td>\n",
       "      <td>UNITED</td>\n",
       "      <td>ORLANDO</td>\n",
       "      <td>East US</td>\n",
       "      <td>Hub</td>\n",
       "      <td>Gates 70-90</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>92.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>2549</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>JETBLUE</td>\n",
       "      <td>LONG BEACH</td>\n",
       "      <td>West US</td>\n",
       "      <td>Small</td>\n",
       "      <td>Gates 1-12</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Somewhat safe</td>\n",
       "      <td>Very satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808</th>\n",
       "      <td>2162</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>CHINA EASTERN</td>\n",
       "      <td>QINGDAO</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Large</td>\n",
       "      <td>Gates 1-12</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Clean</td>\n",
       "      <td>Very safe</td>\n",
       "      <td>Somewhat satsified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2477 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id        day        airline        destination    dest_region  \\\n",
       "0     1351    Tuesday    UNITED INTL             KANSAI           Asia   \n",
       "1      373     Friday         ALASKA  SAN JOSE DEL CABO  Canada/Mexico   \n",
       "2     2820   Thursday          DELTA        LOS ANGELES        West US   \n",
       "3     1157    Tuesday      SOUTHWEST        LOS ANGELES        West US   \n",
       "4     2992  Wednesday       AMERICAN              MIAMI        East US   \n",
       "...    ...        ...            ...                ...            ...   \n",
       "2804  1475    Tuesday         ALASKA       NEW YORK-JFK        East US   \n",
       "2805  2222   Thursday      SOUTHWEST            PHOENIX        West US   \n",
       "2806  2684     Friday         UNITED            ORLANDO        East US   \n",
       "2807  2549    Tuesday        JETBLUE         LONG BEACH        West US   \n",
       "2808  2162   Saturday  CHINA EASTERN            QINGDAO           Asia   \n",
       "\n",
       "     dest_size boarding_area   dept_time  wait_min     cleanliness  \\\n",
       "0          Hub  Gates 91-102  2018-12-31     115.0           Clean   \n",
       "1        Small   Gates 50-59  2018-12-31     135.0           Clean   \n",
       "2          Hub   Gates 40-48  2018-12-31      70.0         Average   \n",
       "3          Hub   Gates 20-39  2018-12-31     190.0           Clean   \n",
       "4          Hub   Gates 50-59  2018-12-31     559.0  Somewhat clean   \n",
       "...        ...           ...         ...       ...             ...   \n",
       "2804       Hub   Gates 50-59  2018-12-31     280.0  Somewhat clean   \n",
       "2805       Hub   Gates 20-39  2018-12-31     165.0           Clean   \n",
       "2806       Hub   Gates 70-90  2018-12-31      92.0           Clean   \n",
       "2807     Small    Gates 1-12  2018-12-31      95.0           Clean   \n",
       "2808     Large    Gates 1-12  2018-12-31     220.0           Clean   \n",
       "\n",
       "             safety        satisfaction  \n",
       "0           Neutral      Very satisfied  \n",
       "1         Very safe      Very satisfied  \n",
       "2     Somewhat safe             Neutral  \n",
       "3         Very safe  Somewhat satsified  \n",
       "4         Very safe  Somewhat satsified  \n",
       "...             ...                 ...  \n",
       "2804        Neutral  Somewhat satsified  \n",
       "2805      Very safe      Very satisfied  \n",
       "2806      Very safe      Very satisfied  \n",
       "2807  Somewhat safe      Very satisfied  \n",
       "2808      Very safe  Somewhat satsified  \n",
       "\n",
       "[2477 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines = pd.read_csv('airlines_final.csv', index_col=0)\n",
    "airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bcf6b455",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Asia' 'Canada/Mexico' 'West US' 'East US' 'Midwest US' 'EAST US'\n",
      " 'Middle East' 'Europe' 'eur' 'Central/South America'\n",
      " 'Australia/New Zealand' 'middle east']\n",
      "['Hub' 'Small' '    Hub' 'Medium' 'Large' 'Hub     ' '    Small'\n",
      " 'Medium     ' '    Medium' 'Small     ' '    Large' 'Large     ']\n",
      "0                asia\n",
      "1       canada/mexico\n",
      "2             west us\n",
      "3             west us\n",
      "4             east us\n",
      "            ...      \n",
      "2804          east us\n",
      "2805          west us\n",
      "2806          east us\n",
      "2807          west us\n",
      "2808             asia\n",
      "Name: dest_region, Length: 2477, dtype: object\n",
      "0         Hub\n",
      "1       Small\n",
      "2         Hub\n",
      "3         Hub\n",
      "4         Hub\n",
      "        ...  \n",
      "2804      Hub\n",
      "2805      Hub\n",
      "2806      Hub\n",
      "2807    Small\n",
      "2808    Large\n",
      "Name: dest_size, Length: 2477, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Print unique values of both columns\n",
    "print(airlines['dest_region'].unique())\n",
    "print(airlines['dest_size'].unique())\n",
    "\n",
    "# Lower dest_region column and then replace \"eur\" with \"europe\"\n",
    "airlines['dest_region'] = airlines['dest_region'].str.lower() \n",
    "airlines['dest_region'] = airlines['dest_region'].replace({'eur':'europe'})\n",
    "\n",
    "# Remove white spaces from `dest_size`\n",
    "airlines['dest_size'] = airlines['dest_size'].str.strip()\n",
    "\n",
    "# Verify changes have been effected\n",
    "print(airlines['dest_region'])\n",
    "print(airlines['dest_size'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2f20dd",
   "metadata": {},
   "source": [
    "The airlines DataFrame contains the day and wait_min columns, which are categorical and numerical respectively. The day column contains the exact day a flight took place, and wait_min contains the amount of minutes it took travelers to wait at the gate. To make your analysis easier, you want to create two new categorical variables:\n",
    "\n",
    "wait_type: 'short' for 0-60 min, 'medium' for 60-180 and long for 180+\n",
    "day_week: 'weekday' if day is in the weekday, 'weekend' if day is in the weekend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac5af4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ranges for categories\n",
    "import numpy as np\n",
    "label_ranges = [0, 60, 180, np.inf]\n",
    "label_names = ['short', 'medium', 'long']\n",
    "\n",
    "# Create wait_type column\n",
    "airlines['wait_type'] = pd.cut(airlines['wait_min'], bins = label_ranges, \n",
    "                                labels = label_names)\n",
    "\n",
    "# Create mappings and replace\n",
    "mappings = {'Monday':'weekday', 'Tuesday':'weekday', 'Wednesday': 'weekday', \n",
    "            'Thursday': 'weekday', 'Friday': 'weekday', \n",
    "            'Saturday': 'weekend', 'Sunday': 'weekend'}\n",
    "\n",
    "airlines['day_week'] = airlines['day'].replace(mappings)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "89d54a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wait_type  day_week\n",
       "medium     weekday     1389\n",
       "long       weekday      537\n",
       "medium     weekend      322\n",
       "long       weekend      148\n",
       "short      weekday       74\n",
       "           weekend        7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airlines[['wait_type', 'day_week']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bad01f",
   "metadata": {},
   "source": [
    "# cleaning text data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "17f20384",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10 entries, 0 to 9\n",
      "Data columns (total 2 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Full Name     10 non-null     object\n",
      " 1   Phone Number  10 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 240.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Phone Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noe Gray</td>\n",
       "      <td>001-887-848-7362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Myles Gomez</td>\n",
       "      <td>001-663-477-0282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gil Silva</td>\n",
       "      <td>001-277-977-7377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prescott Hardin</td>\n",
       "      <td>+1-873-376-1344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benedict</td>\n",
       "      <td>001-277-977-2412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reece</td>\n",
       "      <td>6363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hayfa</td>\n",
       "      <td>001-277-977-7379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hedley</td>\n",
       "      <td>001-277-977-7380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jack</td>\n",
       "      <td>001-277-977-7381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lionel</td>\n",
       "      <td>001-277-977-7382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Full Name      Phone Number\n",
       "0         Noe Gray  001-887-848-7362\n",
       "1      Myles Gomez  001-663-477-0282\n",
       "2        Gil Silva  001-277-977-7377\n",
       "3  Prescott Hardin   +1-873-376-1344\n",
       "4         Benedict  001-277-977-2412\n",
       "5            Reece              6363\n",
       "6            Hayfa  001-277-977-7379\n",
       "7           Hedley  001-277-977-7380\n",
       "8             Jack  001-277-977-7381\n",
       "9           Lionel  001-277-977-7382"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phones = pd.read_csv('phones.csv', index_col=0)\n",
    "phones.info()\n",
    "phones\n",
    "\n",
    "#phone number column contains object type needs to be converted into numbers to be readable\n",
    "#row number 3 and 5 have inconsistent data that nees to be fixed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "af0d64bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-83-ccd314c3fd18>:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  phones['Phone Number'] = phones['Phone Number'].str.replace('+', '00')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    001-887-848-7362\n",
       "1    001-663-477-0282\n",
       "2    001-277-977-7377\n",
       "3    001-873-376-1344\n",
       "4    001-277-977-2412\n",
       "5                6363\n",
       "6    001-277-977-7379\n",
       "7    001-277-977-7380\n",
       "8    001-277-977-7381\n",
       "9    001-277-977-7382\n",
       "Name: Phone Number, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace '+' with '00'\n",
    "\n",
    "phones['Phone Number'] = phones['Phone Number'].str.replace('+', '00')\n",
    "phones['Phone Number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ad7d4d71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0018878487362\n",
       "1    0016634770282\n",
       "2    0012779777377\n",
       "3    0018733761344\n",
       "4    0012779772412\n",
       "5             6363\n",
       "6    0012779777379\n",
       "7    0012779777380\n",
       "8    0012779777381\n",
       "9    0012779777382\n",
       "Name: Phone Number, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing characters from column values\n",
    "phones['Phone Number'] = phones['Phone Number'].str.replace('-', '')\n",
    "phones['Phone Number']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b0658831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    13\n",
      "1    13\n",
      "2    13\n",
      "3    13\n",
      "4    13\n",
      "5     4\n",
      "6    13\n",
      "7    13\n",
      "8    13\n",
      "9    13\n",
      "Name: Phone Number, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Phone Number</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Noe Gray</td>\n",
       "      <td>0018878487362</td>\n",
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       "      <th>1</th>\n",
       "      <td>Myles Gomez</td>\n",
       "      <td>0016634770282</td>\n",
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       "      <th>2</th>\n",
       "      <td>Gil Silva</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prescott Hardin</td>\n",
       "      <td>0018733761344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benedict</td>\n",
       "      <td>0012779772412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reece</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hayfa</td>\n",
       "      <td>0012779777379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hedley</td>\n",
       "      <td>0012779777380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jack</td>\n",
       "      <td>0012779777381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lionel</td>\n",
       "      <td>0012779777382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Full Name   Phone Number\n",
       "0         Noe Gray  0018878487362\n",
       "1      Myles Gomez  0016634770282\n",
       "2        Gil Silva  0012779777377\n",
       "3  Prescott Hardin  0018733761344\n",
       "4         Benedict  0012779772412\n",
       "5            Reece            NaN\n",
       "6            Hayfa  0012779777379\n",
       "7           Hedley  0012779777380\n",
       "8             Jack  0012779777381\n",
       "9           Lionel  0012779777382"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace unwanted values to non (NaN)\n",
    "#replace phone numbers with lesser than 10 digits to NaN\n",
    "\n",
    "import numpy as np\n",
    "digits = phones['Phone Number'].str.len() #len() shows the number of items (lenth) in an object\n",
    "print(digits)\n",
    "phones.loc[digits < 10, 'Phone Number'] = np.nan\n",
    "phones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "48fe1aed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking for corrections made\n",
    "\n",
    "#find length of each row in Phone Number column\n",
    "sanity_check = phones['Phone Number'].str.len()\n",
    "\n",
    "#assert minimum phone number length is 10\n",
    "assert sanity_check.min() >=10\n",
    "\n",
    "#assert all numbers do not have '+' or '-'\n",
    "assert phones['Phone Number'].str.contains('+|-').any() == False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c94c660a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noe Gray</td>\n",
       "      <td>+(1736)-25573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Myles Gomez</td>\n",
       "      <td>+0500-736284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gil Silva</td>\n",
       "      <td>+0800-7263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prescott Hardin</td>\n",
       "      <td>+07253-784653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benedict</td>\n",
       "      <td>+(018345)-9284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Full Name    Phone Number\n",
       "0         Noe Gray   +(1736)-25573\n",
       "1      Myles Gomez    +0500-736284\n",
       "2        Gil Silva      +0800-7263\n",
       "3  Prescott Hardin   +07253-784653\n",
       "4         Benedict  +(018345)-9284"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phones2 = pd.read_csv('phones2.csv', index_col=0)\n",
    "phones2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "e30e00c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-146-b59001395e06>:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  phones2['Phone Number'] = phones2['Phone Number'].str.replace(r'\\D+', '')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Phone Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noe Gray</td>\n",
       "      <td>173625573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Myles Gomez</td>\n",
       "      <td>0500736284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gil Silva</td>\n",
       "      <td>08007263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prescott Hardin</td>\n",
       "      <td>07253784653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Benedict</td>\n",
       "      <td>0183459284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Full Name Phone Number\n",
       "0         Noe Gray    173625573\n",
       "1      Myles Gomez   0500736284\n",
       "2        Gil Silva     08007263\n",
       "3  Prescott Hardin  07253784653\n",
       "4         Benedict   0183459284"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how to remove any text in a object containing numbers\n",
    "phones2['Phone Number'] = phones2['Phone Number'].str.replace(r'\\D+', '') #\n",
    "phones2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f002e121",
   "metadata": {},
   "source": [
    "# Advanced Data Cleaning Problems\n",
    "\n",
    "1. Uniformity\n",
    "   Example: Temparature (C, F), Weight (kg, g), Date (26/11/2009 | 26 November, 2019), Money ($, €)\n",
    "2. Cross Field Validation\n",
    "   The use of multiple fields in a dataset to sanity check data integrity\n",
    "   \n",
    "3. Completeness\n",
    "   Dealing with Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11396c58",
   "metadata": {},
   "source": [
    "# Uniformity: Temperature Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "abcd0c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03.03.19</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04.03.19</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.03.19</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.03.19</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07.03.19</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  Temperature\n",
       "0  03.03.19         14.0\n",
       "1  04.03.19         15.0\n",
       "2  05.03.19         18.0\n",
       "3  06.03.19         16.0\n",
       "4  07.03.19         62.6"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "temperatures\n",
    "\n",
    "#you could notice that there is no uniformity with the temperature values. Row 4 temperature seems to be too high (fahrenheit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bbca3408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using plotting diagram to easily spot the non-uniformity\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x='Date', y='Temperature', data=temperatures)\n",
    "plt.title('Temperature in Celcius March 2019')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Temperature in Celcius')\n",
    "plt.show()\n",
    "\n",
    "#a simple scatter plot visually shows which value is inconsistent and needs to be fixed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f7ff64",
   "metadata": {},
   "source": [
    "Converting Fahrenheit to Celsius\n",
    "Celsius (C) = (F - 32) x 5/9 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "fa6967ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-171-e2ae9119d155>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temperatures['Temperature'][temperatures['Temperature'] > 40] = temp_cels\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03.03.19</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04.03.19</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.03.19</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.03.19</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07.03.19</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  Temperature\n",
       "0  03.03.19         14.0\n",
       "1  04.03.19         15.0\n",
       "2  05.03.19         18.0\n",
       "3  06.03.19         16.0\n",
       "4  07.03.19         17.0"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#treating temperature data so every value is in celcius\n",
    "\n",
    "temp_fah = temperatures['Temperature'][temperatures['Temperature'] > 40]\n",
    "temp_cels = (temp_fah - 32)*(5/9)\n",
    "temperatures['Temperature'][temperatures['Temperature'] > 40] = temp_cels\n",
    "temperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "4f9f2778",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using assert statement to comfirm that the maximum values of Temperature column is less than 40 degrees celsius\n",
    "assert temperatures['Temperature'].max() < 40"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4a14b440",
   "metadata": {},
   "source": [
    "#alternative for treating temperature data so every value is in celcius\n",
    "\n",
    "temp_fah = temperatures.loc[temperatures['Temperature'] > 40, 'Temperature]\n",
    "temp_cels = (temp_fah - 32)*(5/9)\n",
    "temperatures.loc[temperatures['Temperature'] > 40, 'Temperature'] = temp_cels\n",
    "temperatures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a942180d",
   "metadata": {},
   "source": [
    "# Uniformity: Date Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "6208eb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Birthday First Name Last Name\n",
      "0         27/27/19        Row       Nun\n",
      "1         03-29-19      Brynn      Yang\n",
      "2  March 3rd, 2019       Soph       Rei\n",
      "3         24-03-19        Dea    Prince\n",
      "4         06-03-19      Griff      Neal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Birthday      object\n",
       "First Name    object\n",
       "Last Name     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birthdays = pd.read_csv('birthdays.csv', index_col=0)\n",
    "print(birthdays)\n",
    "birthdays.dtypes\n",
    "\n",
    "#there is inconsistency with the datetime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67238b83",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting the datetime formats\n",
    "\n",
    "birthdays['Birthday'] = pd.to_datetime(birthdays['Birthday'])\n",
    "\n",
    "#this will return an error since we have dates in multiple formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "2ca7dfcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Birthday</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaT</td>\n",
       "      <td>Row</td>\n",
       "      <td>Nun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>Brynn</td>\n",
       "      <td>Yang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>Soph</td>\n",
       "      <td>Rei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>Dea</td>\n",
       "      <td>Prince</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>Griff</td>\n",
       "      <td>Neal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Birthday First Name Last Name\n",
       "0        NaT        Row       Nun\n",
       "1 2019-03-29      Brynn      Yang\n",
       "2 2019-03-03       Soph       Rei\n",
       "3 2019-03-24        Dea    Prince\n",
       "4 2019-06-03      Griff      Neal"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correcting the datetime formats\n",
    "\n",
    "birthdays['Birthday'] = pd.to_datetime(birthdays['Birthday'], infer_datetime_format = True, errors = 'coerce')\n",
    "#infer_datetime_format = True >>>>>> attempt to infer format of each date\n",
    "#errors = 'coerce' >>>>>> return NA for rows where conversion failed\n",
    "birthdays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6f5490",
   "metadata": {},
   "source": [
    "Uniformity Practice\n",
    "\n",
    "Uniform currencies\n",
    "In this exercise and throughout this chapter, you will be working with a retail banking dataset stored in the banking DataFrame. The dataset contains data on the amount of money stored in accounts (acct_amount), their currency (acct_cur), amount invested (inv_amount), account opening date (account_opened), and last transaction date (last_transaction) that were consolidated from American and European branches.\n",
    "\n",
    "You are tasked with understanding the average account size and how investments vary by the size of account, however in order to produce this analysis accurately, you first need to unify the currency amount into dollars. The pandas package has been imported as pd, and the banking DataFrame is in your environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "aac7e042",
   "metadata": {},
   "outputs": [
    {
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       "      <th>birth_date</th>\n",
       "      <th>Age</th>\n",
       "      <th>acct_amount</th>\n",
       "      <th>inv_amount</th>\n",
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       "      <th>fund_B</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>870A9281</td>\n",
       "      <td>1962-06-09</td>\n",
       "      <td>58</td>\n",
       "      <td>63523.31</td>\n",
       "      <td>51295</td>\n",
       "      <td>30105.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>15632.0</td>\n",
       "      <td>02-09-18</td>\n",
       "      <td>22-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166B05B0</td>\n",
       "      <td>1962-12-16</td>\n",
       "      <td>58</td>\n",
       "      <td>38175.46</td>\n",
       "      <td>15050</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>6696.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>28-02-19</td>\n",
       "      <td>31-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BFC13E88</td>\n",
       "      <td>1990-09-12</td>\n",
       "      <td>34</td>\n",
       "      <td>59863.77</td>\n",
       "      <td>24567</td>\n",
       "      <td>10323.0</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>8469.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>25-04-18</td>\n",
       "      <td>02-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2158F66</td>\n",
       "      <td>1985-11-03</td>\n",
       "      <td>35</td>\n",
       "      <td>84132.10</td>\n",
       "      <td>23712</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>6482.0</td>\n",
       "      <td>12830.0</td>\n",
       "      <td>07-11-17</td>\n",
       "      <td>08-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7A73F334</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>30</td>\n",
       "      <td>120512.00</td>\n",
       "      <td>93230</td>\n",
       "      <td>12158.4</td>\n",
       "      <td>51281.0</td>\n",
       "      <td>13434.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>14-05-18</td>\n",
       "      <td>19-07-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>CA507BA1</td>\n",
       "      <td>1974-08-10</td>\n",
       "      <td>46</td>\n",
       "      <td>12209.84</td>\n",
       "      <td>7515</td>\n",
       "      <td>190.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>4943.0</td>\n",
       "      <td>26-05-18</td>\n",
       "      <td>11-09-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>B99CD662</td>\n",
       "      <td>1989-12-12</td>\n",
       "      <td>31</td>\n",
       "      <td>92838.44</td>\n",
       "      <td>49089</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>7892.0</td>\n",
       "      <td>31486.0</td>\n",
       "      <td>7258.0</td>\n",
       "      <td>04-05-17</td>\n",
       "      <td>12-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>13770971</td>\n",
       "      <td>1984-11-29</td>\n",
       "      <td>36</td>\n",
       "      <td>92750.87</td>\n",
       "      <td>27962</td>\n",
       "      <td>3352.0</td>\n",
       "      <td>7547.0</td>\n",
       "      <td>8486.0</td>\n",
       "      <td>8577.0</td>\n",
       "      <td>16-08-17</td>\n",
       "      <td>24-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>93E78DA3</td>\n",
       "      <td>1969-12-14</td>\n",
       "      <td>51</td>\n",
       "      <td>41942.23</td>\n",
       "      <td>29662</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>11174.0</td>\n",
       "      <td>11650.0</td>\n",
       "      <td>5080.0</td>\n",
       "      <td>09-10-17</td>\n",
       "      <td>15-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>AC91D689</td>\n",
       "      <td>1993-05-18</td>\n",
       "      <td>27</td>\n",
       "      <td>99490.61</td>\n",
       "      <td>32149</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>17918.0</td>\n",
       "      <td>6714.0</td>\n",
       "      <td>5333.0</td>\n",
       "      <td>01-08-17</td>\n",
       "      <td>04-08-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cust_id  birth_date  Age  acct_amount  inv_amount   fund_A   fund_B  \\\n",
       "0   870A9281  1962-06-09   58     63523.31       51295  30105.0   4138.0   \n",
       "1   166B05B0  1962-12-16   58     38175.46       15050   4995.0    938.0   \n",
       "2   BFC13E88  1990-09-12   34     59863.77       24567  10323.0   4590.0   \n",
       "3   F2158F66  1985-11-03   35     84132.10       23712   3908.0    492.0   \n",
       "4   7A73F334  1990-05-17   30    120512.00       93230  12158.4  51281.0   \n",
       "..       ...         ...  ...          ...         ...      ...      ...   \n",
       "95  CA507BA1  1974-08-10   46     12209.84        7515    190.0    931.0   \n",
       "96  B99CD662  1989-12-12   31     92838.44       49089   2453.0   7892.0   \n",
       "97  13770971  1984-11-29   36     92750.87       27962   3352.0   7547.0   \n",
       "98  93E78DA3  1969-12-14   51     41942.23       29662   1758.0  11174.0   \n",
       "99  AC91D689  1993-05-18   27     99490.61       32149   2184.0  17918.0   \n",
       "\n",
       "     fund_C   fund_D account_opened last_transaction  \n",
       "0    1420.0  15632.0       02-09-18         22-02-19  \n",
       "1    6696.0   2421.0       28-02-19         31-10-18  \n",
       "2    8469.0   1185.0       25-04-18         02-04-18  \n",
       "3    6482.0  12830.0       07-11-17         08-11-18  \n",
       "4   13434.0  18383.0       14-05-18         19-07-18  \n",
       "..      ...      ...            ...              ...  \n",
       "95   1451.0   4943.0       26-05-18         11-09-19  \n",
       "96  31486.0   7258.0       04-05-17         12-03-19  \n",
       "97   8486.0   8577.0       16-08-17         24-04-19  \n",
       "98  11650.0   5080.0       09-10-17         15-04-18  \n",
       "99   6714.0   5333.0       01-08-17         04-08-19  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banking = pd.read_csv('banking_dirty.csv', index_col=0)\n",
    "banking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "41d43177",
   "metadata": {},
   "outputs": [
    {
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       "      <td>15050</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>6696.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>2018-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BFC13E88</td>\n",
       "      <td>1990-09-12</td>\n",
       "      <td>34</td>\n",
       "      <td>59863.77</td>\n",
       "      <td>24567</td>\n",
       "      <td>10323.0</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>8469.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>2018-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2158F66</td>\n",
       "      <td>1985-11-03</td>\n",
       "      <td>35</td>\n",
       "      <td>84132.10</td>\n",
       "      <td>23712</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>6482.0</td>\n",
       "      <td>12830.0</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7A73F334</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>30</td>\n",
       "      <td>120512.00</td>\n",
       "      <td>93230</td>\n",
       "      <td>12158.4</td>\n",
       "      <td>51281.0</td>\n",
       "      <td>13434.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>2018-05-14</td>\n",
       "      <td>2018-07-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cust_id  birth_date  Age  acct_amount  inv_amount   fund_A   fund_B  \\\n",
       "0  870A9281  1962-06-09   58     63523.31       51295  30105.0   4138.0   \n",
       "1  166B05B0  1962-12-16   58     38175.46       15050   4995.0    938.0   \n",
       "2  BFC13E88  1990-09-12   34     59863.77       24567  10323.0   4590.0   \n",
       "3  F2158F66  1985-11-03   35     84132.10       23712   3908.0    492.0   \n",
       "4  7A73F334  1990-05-17   30    120512.00       93230  12158.4  51281.0   \n",
       "\n",
       "    fund_C   fund_D account_opened last_transaction  \n",
       "0   1420.0  15632.0     2018-02-09       2019-02-22  \n",
       "1   6696.0   2421.0     2019-02-28       2018-10-31  \n",
       "2   8469.0   1185.0     2018-04-25       2018-02-04  \n",
       "3   6482.0  12830.0     2017-07-11       2018-08-11  \n",
       "4  13434.0  18383.0     2018-05-14       2018-07-19  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing the datetime format of banking dataframe\n",
    "\n",
    "banking['account_opened'] = pd.to_datetime(banking['account_opened'])\n",
    "banking['last_transaction'] = pd.to_datetime(banking['last_transaction'])\n",
    "banking.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a04030",
   "metadata": {},
   "outputs": [],
   "source": [
    "banking['acct_year'] = banking['account_opened'].dt."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25dd3810",
   "metadata": {},
   "source": [
    "# Cross Field Validation for Total Sum Integrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "cfd78a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flight_number</th>\n",
       "      <th>economy_class</th>\n",
       "      <th>business_class</th>\n",
       "      <th>first_class</th>\n",
       "      <th>total_passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GJS7737</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JRGH267</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DFH7723</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GUFSJ23</td>\n",
       "      <td>140</td>\n",
       "      <td>70</td>\n",
       "      <td>90</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HEF7736</td>\n",
       "      <td>130</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  flight_number  economy_class  business_class  first_class  total_passengers\n",
       "0       GJS7737            100              60           40               200\n",
       "1       JRGH267            130             100           70               300\n",
       "2       DFH7723            100              50           50               200\n",
       "3       GUFSJ23            140              70           90               300\n",
       "4       HEF7736            130             100           20               250"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_csv('flights.csv', index_col=0)\n",
    "flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "96b0eebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    200\n",
      "1    300\n",
      "2    200\n",
      "3    300\n",
      "4    250\n",
      "dtype: int64\n",
      "0    True\n",
      "1    True\n",
      "2    True\n",
      "3    True\n",
      "4    True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "#checking if the total_passengers column equals the summation of values from economy_class, business_class, and first_class columns\n",
    "\n",
    "sum_classes = flights[['economy_class', 'business_class', 'first_class']].sum(axis=1)\n",
    "print(sum_classes)\n",
    "#axis=1 is used to indicate row wise summing instead of column wise summing\n",
    "\n",
    "passenger_equ = sum_classes == flights['total_passengers']\n",
    "print(passenger_equ)\n",
    "\n",
    "#find and filter out rows with inconsistent passengers\n",
    "inconsistent_pass = flights[~passenger_equ]\n",
    "consistent_pass = flights[passenger_equ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3000806",
   "metadata": {},
   "source": [
    "# Cross Field Validation for Age Integrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "19f679e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8276</td>\n",
       "      <td>22</td>\n",
       "      <td>1998-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3864</td>\n",
       "      <td>27</td>\n",
       "      <td>1993-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9272</td>\n",
       "      <td>42</td>\n",
       "      <td>1978-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2535</td>\n",
       "      <td>31</td>\n",
       "      <td>1989-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8747</td>\n",
       "      <td>18</td>\n",
       "      <td>2002-07-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age    birthday\n",
       "0     8276   22  1998-03-02\n",
       "1     3864   27  1993-12-04\n",
       "2     9272   42  1978-11-24\n",
       "3     2535   31  1989-01-03\n",
       "4     8747   18  2002-07-02"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = pd.read_csv('users.csv', index_col=0)\n",
    "users "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "a81f60e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    24\n",
      "1    29\n",
      "2    44\n",
      "3    33\n",
      "4    20\n",
      "Name: birthday, dtype: int64\n",
      "0    True\n",
      "1    True\n",
      "2    True\n",
      "3    True\n",
      "4    True\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "#checking if the ages are correct in todays time from the date of birth given\n",
    "#convert t datetime and get today's date\n",
    "import datetime as dt\n",
    "\n",
    "users['birthday'] = pd.to_datetime(users['birthday'])\n",
    "today = dt.date.today()\n",
    "\n",
    "#for each row in the birthday column, calculate the year difference\n",
    "age_manual = today.year - users.birthday.dt.year\n",
    "print(age_manual)\n",
    "\n",
    "#find instances where ages match\n",
    "age_match = users.age == age_manual\n",
    "print(age_match)\n",
    "\n",
    "inconsistent_age = users[~age_match]\n",
    "consistent_age = users[age_match]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "3cad556a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8276</td>\n",
       "      <td>24</td>\n",
       "      <td>1998-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3864</td>\n",
       "      <td>29</td>\n",
       "      <td>1993-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9272</td>\n",
       "      <td>44</td>\n",
       "      <td>1978-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2535</td>\n",
       "      <td>33</td>\n",
       "      <td>1989-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8747</td>\n",
       "      <td>20</td>\n",
       "      <td>2002-07-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age   birthday\n",
       "0     8276   24 1998-03-02\n",
       "1     3864   29 1993-12-04\n",
       "2     9272   44 1978-11-24\n",
       "3     2535   33 1989-01-03\n",
       "4     8747   20 2002-07-02"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replacing age column with the right (new) age values\n",
    "users['age'] = age_manual\n",
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5144ecf5",
   "metadata": {},
   "source": [
    "New data has been merged into the banking DataFrame that contains details on how investments in the inv_amount column are allocated across four different funds A, B, C and D.\n",
    "\n",
    "Furthermore, the age and birthdays of customers are now stored in the age and birth_date columns respectively.\n",
    "\n",
    "You want to understand how customers of different age groups invest. However, you want to first make sure the data you're analyzing is correct. You will do so by cross field checking values of inv_amount and age against the amount invested in different funds and customers' birthdays. Both pandas and datetime have been imported as pd and dt respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "d200974e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>Age</th>\n",
       "      <th>acct_amount</th>\n",
       "      <th>inv_amount</th>\n",
       "      <th>fund_A</th>\n",
       "      <th>fund_B</th>\n",
       "      <th>fund_C</th>\n",
       "      <th>fund_D</th>\n",
       "      <th>account_opened</th>\n",
       "      <th>last_transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>870A9281</td>\n",
       "      <td>1962-06-09</td>\n",
       "      <td>58</td>\n",
       "      <td>63523.31</td>\n",
       "      <td>51295</td>\n",
       "      <td>30105.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>15632.0</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>2019-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166B05B0</td>\n",
       "      <td>1962-12-16</td>\n",
       "      <td>58</td>\n",
       "      <td>38175.46</td>\n",
       "      <td>15050</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>6696.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>2019-02-28</td>\n",
       "      <td>2018-10-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BFC13E88</td>\n",
       "      <td>1990-09-12</td>\n",
       "      <td>34</td>\n",
       "      <td>59863.77</td>\n",
       "      <td>24567</td>\n",
       "      <td>10323.0</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>8469.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>2018-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2158F66</td>\n",
       "      <td>1985-11-03</td>\n",
       "      <td>35</td>\n",
       "      <td>84132.10</td>\n",
       "      <td>23712</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>6482.0</td>\n",
       "      <td>12830.0</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>2018-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7A73F334</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>30</td>\n",
       "      <td>120512.00</td>\n",
       "      <td>93230</td>\n",
       "      <td>12158.4</td>\n",
       "      <td>51281.0</td>\n",
       "      <td>13434.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>2018-05-14</td>\n",
       "      <td>2018-07-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>CA507BA1</td>\n",
       "      <td>1974-08-10</td>\n",
       "      <td>46</td>\n",
       "      <td>12209.84</td>\n",
       "      <td>7515</td>\n",
       "      <td>190.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>4943.0</td>\n",
       "      <td>2018-05-26</td>\n",
       "      <td>2019-11-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>B99CD662</td>\n",
       "      <td>1989-12-12</td>\n",
       "      <td>31</td>\n",
       "      <td>92838.44</td>\n",
       "      <td>49089</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>7892.0</td>\n",
       "      <td>31486.0</td>\n",
       "      <td>7258.0</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>2019-12-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>13770971</td>\n",
       "      <td>1984-11-29</td>\n",
       "      <td>36</td>\n",
       "      <td>92750.87</td>\n",
       "      <td>27962</td>\n",
       "      <td>3352.0</td>\n",
       "      <td>7547.0</td>\n",
       "      <td>8486.0</td>\n",
       "      <td>8577.0</td>\n",
       "      <td>2017-08-16</td>\n",
       "      <td>2019-04-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>93E78DA3</td>\n",
       "      <td>1969-12-14</td>\n",
       "      <td>51</td>\n",
       "      <td>41942.23</td>\n",
       "      <td>29662</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>11174.0</td>\n",
       "      <td>11650.0</td>\n",
       "      <td>5080.0</td>\n",
       "      <td>2017-09-10</td>\n",
       "      <td>2018-04-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>AC91D689</td>\n",
       "      <td>1993-05-18</td>\n",
       "      <td>27</td>\n",
       "      <td>99490.61</td>\n",
       "      <td>32149</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>17918.0</td>\n",
       "      <td>6714.0</td>\n",
       "      <td>5333.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>2019-04-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cust_id  birth_date  Age  acct_amount  inv_amount   fund_A   fund_B  \\\n",
       "0   870A9281  1962-06-09   58     63523.31       51295  30105.0   4138.0   \n",
       "1   166B05B0  1962-12-16   58     38175.46       15050   4995.0    938.0   \n",
       "2   BFC13E88  1990-09-12   34     59863.77       24567  10323.0   4590.0   \n",
       "3   F2158F66  1985-11-03   35     84132.10       23712   3908.0    492.0   \n",
       "4   7A73F334  1990-05-17   30    120512.00       93230  12158.4  51281.0   \n",
       "..       ...         ...  ...          ...         ...      ...      ...   \n",
       "95  CA507BA1  1974-08-10   46     12209.84        7515    190.0    931.0   \n",
       "96  B99CD662  1989-12-12   31     92838.44       49089   2453.0   7892.0   \n",
       "97  13770971  1984-11-29   36     92750.87       27962   3352.0   7547.0   \n",
       "98  93E78DA3  1969-12-14   51     41942.23       29662   1758.0  11174.0   \n",
       "99  AC91D689  1993-05-18   27     99490.61       32149   2184.0  17918.0   \n",
       "\n",
       "     fund_C   fund_D account_opened last_transaction  \n",
       "0    1420.0  15632.0     2018-02-09       2019-02-22  \n",
       "1    6696.0   2421.0     2019-02-28       2018-10-31  \n",
       "2    8469.0   1185.0     2018-04-25       2018-02-04  \n",
       "3    6482.0  12830.0     2017-07-11       2018-08-11  \n",
       "4   13434.0  18383.0     2018-05-14       2018-07-19  \n",
       "..      ...      ...            ...              ...  \n",
       "95   1451.0   4943.0     2018-05-26       2019-11-09  \n",
       "96  31486.0   7258.0     2017-04-05       2019-12-03  \n",
       "97   8486.0   8577.0     2017-08-16       2019-04-24  \n",
       "98  11650.0   5080.0     2017-09-10       2018-04-15  \n",
       "99   6714.0   5333.0     2017-01-08       2019-04-08  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "ded25dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      True\n",
      "1      True\n",
      "2      True\n",
      "3      True\n",
      "4     False\n",
      "      ...  \n",
      "95     True\n",
      "96     True\n",
      "97     True\n",
      "98     True\n",
      "99     True\n",
      "Length: 100, dtype: bool\n",
      "Number of inconsistent investments:  8\n"
     ]
    }
   ],
   "source": [
    "# Store fund columns to sum against\n",
    "fund_columns = ['fund_A', 'fund_B', 'fund_C', 'fund_D']\n",
    "\n",
    "# Find rows where fund_columns row sum == inv_amount\n",
    "inv_equ = banking[fund_columns].sum(axis=1) == banking['inv_amount']\n",
    "print(inv_equ)\n",
    "# Store consistent and inconsistent data\n",
    "consistent_inv = banking[inv_equ]\n",
    "inconsistent_inv = banking[~inv_equ]\n",
    "\n",
    "# Store consistent and inconsistent data\n",
    "print(\"Number of inconsistent investments: \", inconsistent_inv.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "fc32b882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-03-13\n",
      "0     60\n",
      "1     60\n",
      "2     32\n",
      "3     37\n",
      "4     32\n",
      "      ..\n",
      "95    48\n",
      "96    33\n",
      "97    38\n",
      "98    53\n",
      "99    29\n",
      "Name: birth_date, Length: 100, dtype: int64\n",
      "Number of inconsistent ages:  100\n"
     ]
    }
   ],
   "source": [
    "#changing the datetime format of banking dataframe\n",
    "banking['birth_date'] = pd.to_datetime(banking['birth_date'])\n",
    "\n",
    "# Store today's date and find ages\n",
    "today = dt.date.today()\n",
    "print(today)\n",
    "ages_manual = today.year - banking['birth_date'].dt.year\n",
    "print(ages_manual)\n",
    "\n",
    "# Find rows where age column == ages_manual\n",
    "age_equ = banking['Age'] == ages_manual\n",
    "\n",
    "# Store consistent and inconsistent data\n",
    "consistent_ages = banking[age_equ]\n",
    "inconsistent_ages = banking[~age_equ]\n",
    "\n",
    "# Store consistent and inconsistent data\n",
    "print(\"Number of inconsistent ages: \", inconsistent_ages.shape[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1873a62",
   "metadata": {},
   "source": [
    "# Completeness: Dealing with Missing Data\n",
    "\n",
    "Missing data can represented as NaN, nan, 0, ., etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "430504ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>20/04/2004</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>7/6/2004</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>20/06/2004</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1/6/2004</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8299</th>\n",
       "      <td>19/02/2005</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  temperature  co2\n",
       "987   20/04/2004         16.8  0.0\n",
       "2119    7/6/2004         18.7  0.8\n",
       "2451  20/06/2004        -40.0  NaN\n",
       "1984    1/6/2004         19.6  1.8\n",
       "8299  19/02/2005         11.2  1.2"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airquality = pd.read_csv('airquality.csv', index_col=0)\n",
    "airquality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b87d8b19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       date  temperature    co2\n",
      "987   False        False  False\n",
      "2119  False        False  False\n",
      "2451  False        False   True\n",
      "1984  False        False  False\n",
      "8299  False        False  False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "date           0\n",
       "temperature    0\n",
       "co2            1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#searching for missing values\n",
    "print(airquality.isna())\n",
    "#shows co2 column has a missing value\n",
    "#returns True for missing values and False for complete values\n",
    "\n",
    "#showing the number of missing values per column\n",
    "airquality.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf92fa1",
   "metadata": {},
   "source": [
    "Missingno is a useful package for visualizing and understanding missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d1665410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#visualize missingness using matrix\n",
    "msno.matrix(airquality)\n",
    "plt.show()\n",
    "\n",
    "#the matrix shows how missing values are distributed accross a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "26e46394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing missing data using bar plot\n",
    "msno.bar(airquality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "32a2d730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing missing data using dendrogram\n",
    "msno.dendrogram(airquality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b34feee0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            date  temperature  co2\n",
      "2451  20/06/2004        -40.0  NaN \n",
      "\n",
      "            date  temperature  co2\n",
      "987   20/04/2004         16.8  0.0\n",
      "2119    7/6/2004         18.7  0.8\n",
      "1984    1/6/2004         19.6  1.8\n",
      "8299  19/02/2005         11.2  1.2 \n",
      "\n",
      "       temperature  co2\n",
      "count          1.0  0.0\n",
      "mean         -40.0  NaN\n",
      "std            NaN  NaN\n",
      "min          -40.0  NaN\n",
      "25%          -40.0  NaN\n",
      "50%          -40.0  NaN\n",
      "75%          -40.0  NaN\n",
      "max          -40.0  NaN \n",
      "\n",
      "       temperature       co2\n",
      "count      4.00000  4.000000\n",
      "mean      16.57500  0.950000\n",
      "std        3.76862  0.754983\n",
      "min       11.20000  0.000000\n",
      "25%       15.40000  0.600000\n",
      "50%       17.75000  1.000000\n",
      "75%       18.92500  1.350000\n",
      "max       19.60000  1.800000\n"
     ]
    }
   ],
   "source": [
    "#isolate missing and complete values aside\n",
    "\n",
    "missing = airquality[airquality['co2'].isna()]\n",
    "print(missing, '\\n')\n",
    "\n",
    "complete = airquality[~airquality['co2'].isna()]\n",
    "print(complete, '\\n')\n",
    "\n",
    "print(missing.describe(), '\\n')\n",
    "print(complete.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "adcfeaac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sorting missing values with matrix plot\n",
    "sorted_airquality = airquality.sort_values(by='temperature')\n",
    "msno.matrix(sorted_airquality)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51b7348",
   "metadata": {},
   "source": [
    "# Cleaning Missing Data/Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "28e831f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>20/04/2004</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>7/6/2004</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1/6/2004</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8299</th>\n",
       "      <td>19/02/2005</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  temperature  co2\n",
       "987   20/04/2004         16.8  0.0\n",
       "2119    7/6/2004         18.7  0.8\n",
       "1984    1/6/2004         19.6  1.8\n",
       "8299  19/02/2005         11.2  1.2"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop missing data\n",
    "airquality_dropped = airquality.dropna(subset=['co2'])\n",
    "airquality_dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6c0a562b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>20/04/2004</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>7/6/2004</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>20/06/2004</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1/6/2004</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8299</th>\n",
       "      <td>19/02/2005</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  temperature  co2\n",
       "987   20/04/2004         16.8  0.0\n",
       "2119    7/6/2004         18.7  0.8\n",
       "2451  20/06/2004        -40.0  NaN\n",
       "1984    1/6/2004         19.6  1.8\n",
       "8299  19/02/2005         11.2  1.2"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replacing missing data with statistical measures\n",
    "co2_mean = airquality['co2'].mean()\n",
    "airquality_imputed = airquality.fillna({'c02': co2_mean})\n",
    "airquality_imputed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4738f94",
   "metadata": {},
   "source": [
    "# missing data practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "13fd35a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>Age</th>\n",
       "      <th>acct_amount</th>\n",
       "      <th>inv_amount</th>\n",
       "      <th>fund_A</th>\n",
       "      <th>fund_B</th>\n",
       "      <th>fund_C</th>\n",
       "      <th>fund_D</th>\n",
       "      <th>account_opened</th>\n",
       "      <th>last_transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>870A9281</td>\n",
       "      <td>1962-06-09</td>\n",
       "      <td>58</td>\n",
       "      <td>63523.31</td>\n",
       "      <td>51295</td>\n",
       "      <td>30105.0</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>15632.0</td>\n",
       "      <td>02-09-18</td>\n",
       "      <td>22-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166B05B0</td>\n",
       "      <td>1962-12-16</td>\n",
       "      <td>58</td>\n",
       "      <td>38175.46</td>\n",
       "      <td>15050</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>6696.0</td>\n",
       "      <td>2421.0</td>\n",
       "      <td>28-02-19</td>\n",
       "      <td>31-10-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BFC13E88</td>\n",
       "      <td>1990-09-12</td>\n",
       "      <td>34</td>\n",
       "      <td>59863.77</td>\n",
       "      <td>24567</td>\n",
       "      <td>10323.0</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>8469.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>25-04-18</td>\n",
       "      <td>02-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2158F66</td>\n",
       "      <td>1985-11-03</td>\n",
       "      <td>35</td>\n",
       "      <td>84132.10</td>\n",
       "      <td>23712</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>6482.0</td>\n",
       "      <td>12830.0</td>\n",
       "      <td>07-11-17</td>\n",
       "      <td>08-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7A73F334</td>\n",
       "      <td>1990-05-17</td>\n",
       "      <td>30</td>\n",
       "      <td>120512.00</td>\n",
       "      <td>93230</td>\n",
       "      <td>12158.4</td>\n",
       "      <td>51281.0</td>\n",
       "      <td>13434.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>14-05-18</td>\n",
       "      <td>19-07-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>CA507BA1</td>\n",
       "      <td>1974-08-10</td>\n",
       "      <td>46</td>\n",
       "      <td>12209.84</td>\n",
       "      <td>7515</td>\n",
       "      <td>190.0</td>\n",
       "      <td>931.0</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>4943.0</td>\n",
       "      <td>26-05-18</td>\n",
       "      <td>11-09-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>B99CD662</td>\n",
       "      <td>1989-12-12</td>\n",
       "      <td>31</td>\n",
       "      <td>92838.44</td>\n",
       "      <td>49089</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>7892.0</td>\n",
       "      <td>31486.0</td>\n",
       "      <td>7258.0</td>\n",
       "      <td>04-05-17</td>\n",
       "      <td>12-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>13770971</td>\n",
       "      <td>1984-11-29</td>\n",
       "      <td>36</td>\n",
       "      <td>92750.87</td>\n",
       "      <td>27962</td>\n",
       "      <td>3352.0</td>\n",
       "      <td>7547.0</td>\n",
       "      <td>8486.0</td>\n",
       "      <td>8577.0</td>\n",
       "      <td>16-08-17</td>\n",
       "      <td>24-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>93E78DA3</td>\n",
       "      <td>1969-12-14</td>\n",
       "      <td>51</td>\n",
       "      <td>41942.23</td>\n",
       "      <td>29662</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>11174.0</td>\n",
       "      <td>11650.0</td>\n",
       "      <td>5080.0</td>\n",
       "      <td>09-10-17</td>\n",
       "      <td>15-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>AC91D689</td>\n",
       "      <td>1993-05-18</td>\n",
       "      <td>27</td>\n",
       "      <td>99490.61</td>\n",
       "      <td>32149</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>17918.0</td>\n",
       "      <td>6714.0</td>\n",
       "      <td>5333.0</td>\n",
       "      <td>01-08-17</td>\n",
       "      <td>04-08-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cust_id  birth_date  Age  acct_amount  inv_amount   fund_A   fund_B  \\\n",
       "0   870A9281  1962-06-09   58     63523.31       51295  30105.0   4138.0   \n",
       "1   166B05B0  1962-12-16   58     38175.46       15050   4995.0    938.0   \n",
       "2   BFC13E88  1990-09-12   34     59863.77       24567  10323.0   4590.0   \n",
       "3   F2158F66  1985-11-03   35     84132.10       23712   3908.0    492.0   \n",
       "4   7A73F334  1990-05-17   30    120512.00       93230  12158.4  51281.0   \n",
       "..       ...         ...  ...          ...         ...      ...      ...   \n",
       "95  CA507BA1  1974-08-10   46     12209.84        7515    190.0    931.0   \n",
       "96  B99CD662  1989-12-12   31     92838.44       49089   2453.0   7892.0   \n",
       "97  13770971  1984-11-29   36     92750.87       27962   3352.0   7547.0   \n",
       "98  93E78DA3  1969-12-14   51     41942.23       29662   1758.0  11174.0   \n",
       "99  AC91D689  1993-05-18   27     99490.61       32149   2184.0  17918.0   \n",
       "\n",
       "     fund_C   fund_D account_opened last_transaction  \n",
       "0    1420.0  15632.0       02-09-18         22-02-19  \n",
       "1    6696.0   2421.0       28-02-19         31-10-18  \n",
       "2    8469.0   1185.0       25-04-18         02-04-18  \n",
       "3    6482.0  12830.0       07-11-17         08-11-18  \n",
       "4   13434.0  18383.0       14-05-18         19-07-18  \n",
       "..      ...      ...            ...              ...  \n",
       "95   1451.0   4943.0       26-05-18         11-09-19  \n",
       "96  31486.0   7258.0       04-05-17         12-03-19  \n",
       "97   8486.0   8577.0       16-08-17         24-04-19  \n",
       "98  11650.0   5080.0       09-10-17         15-04-18  \n",
       "99   6714.0   5333.0       01-08-17         04-08-19  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "43efb7e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cust_id             0\n",
      "birth_date          0\n",
      "Age                 0\n",
      "acct_amount         0\n",
      "inv_amount          0\n",
      "fund_A              0\n",
      "fund_B              0\n",
      "fund_C              0\n",
      "fund_D              0\n",
      "account_opened      0\n",
      "last_transaction    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print number of missing values in banking\n",
    "print(banking.isna().sum())\n",
    "\n",
    "# Visualize missingness matrix\n",
    "msno.matrix(banking)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b11c2f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Isolate missing and non missing values of inv_amount\n",
    "missing_investors = banking[banking['inv_amount'].isna()]\n",
    "investors = banking[~banking['inv_amount'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c4384940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort banking by age and visualize\n",
    "banking_sorted = banking.sort_values(by=['Age'])\n",
    "msno.matrix(banking_sorted)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b4ecfc",
   "metadata": {},
   "source": [
    "# Comparing Strings: Record Linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "27c541c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comparing between 2 strings\n",
    "from fuzzywuzzy import fuzz\n",
    "\n",
    "#compare reeding vs reading\n",
    "fuzz.WRatio('Reeding', 'Reading')\n",
    "\n",
    "#ratio ranges from 1 to 100. 100 being a complete match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "74b2372d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#partial string comparison\n",
    "\n",
    "fuzz.WRatio('Houston Rockets', 'Rockets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "dae71220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#partial string comparison with different order\n",
    "\n",
    "fuzz.WRatio('Houston Rockets vs Los Angeles Lakers', 'Lakers vs Rockets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "bdd47ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Rockets vs Lakers', 86, 0), ('Lakers vs Rockets', 86, 1)]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comparison with arrays\n",
    "\n",
    "#import process\n",
    "from fuzzywuzzy import process\n",
    "\n",
    "#define strings and array of possible matches\n",
    "string = 'Houston Rockets vs Los Angeles Lakers'\n",
    "choices = pd.Series(['Rockets vs Lakers', 'Lakers vs Rockets', 'Houston vs Los Angeles', 'Heat vs Bulls'])\n",
    "\n",
    "process.extract(string, choices, limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "25b3960d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Rockets vs Lakers', 86, 0),\n",
       " ('Lakers vs Rockets', 86, 1),\n",
       " ('Houston vs Los Angeles', 86, 2),\n",
       " ('Heat vs Bulls', 86, 3)]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string = 'Houston Rockets vs Los Angeles Lakers'\n",
    "choices = pd.Series(['Rockets vs Lakers', 'Lakers vs Rockets', 'Houston vs Los Angeles', 'Heat vs Bulls'])\n",
    "\n",
    "process.extract(string, choices, limit=len(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "de18cf12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            state\n",
      "id               \n",
      "0      California\n",
      "1            Cali\n",
      "2      Calefornia\n",
      "3      Calefornie\n",
      "4      Californie\n",
      "5       Calfornia\n",
      "6      Calefernia\n",
      "7        New York\n",
      "8   New York City\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state\n",
       "0  California\n",
       "1    New York"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#collapsing categories with string matching\n",
    "\n",
    "survey = pd.read_csv('survey.csv', index_col=0)\n",
    "print(survey)\n",
    "\n",
    "#creating categories dataset with the right spellings\n",
    "categories2 = pd.read_csv('categories2.csv', index_col=0)\n",
    "categories2\n",
    "\n",
    "#the state column of survey dataset has a lot of mispelled names that need to be corrected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "13350c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state\n",
       "id            \n",
       "0   California\n",
       "1   California\n",
       "2   California\n",
       "3   California\n",
       "4   California\n",
       "5   California\n",
       "6   California\n",
       "7     New York\n",
       "8     New York"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fuzzywuzzy import process\n",
    "\n",
    "#for each correct category\n",
    "for state in categories2['state']:\n",
    "    #find potential matches in state with typoes\n",
    "    matches = process.extract(state, survey['state'], limit=survey.shape[0])\n",
    "    #for each potential match match\n",
    "    for potential_match in matches:\n",
    "        #if high similarity score\n",
    "        if potential_match[1] >= 80:\n",
    "            #replace typo with correct category\n",
    "            survey.loc[survey['state'] == potential_match[0], 'state'] = state\n",
    "            \n",
    "survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "dd52f969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('California', 100, 0),\n",
       " ('Calfornia', 95, 5),\n",
       " ('Cali', 90, 1),\n",
       " ('Calefornia', 90, 2),\n",
       " ('Californie', 90, 4),\n",
       " ('Calefornie', 80, 3),\n",
       " ('Calefernia', 80, 6),\n",
       " ('New York City', 33, 8),\n",
       " ('New York', 22, 7)]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = process.extract('California', survey['state'], limit = len(survey))\n",
    "matches"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cc359ed6",
   "metadata": {},
   "source": [
    "# Comparing Strings: Record Linkage \n",
    "\n",
    "Record linkage is the act of linking data from different sources regarding the same entity. Merging data has to be done carefully to avoid duplication. \n",
    "\n",
    "The process of record linkage is as follows:\n",
    "\n",
    "1. Extract 2 or more data. eg, Data A and Data B\n",
    "2. Generate pairs\n",
    "3. Compare pairs\n",
    "4. Score pairs\n",
    "5. Link Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a79d5351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>suburb</th>\n",
       "      <th>state</th>\n",
       "      <th>address_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rec-1070-org</th>\n",
       "      <td>michaela</td>\n",
       "      <td>neuman</td>\n",
       "      <td>19151111</td>\n",
       "      <td>winston hills</td>\n",
       "      <td>cal</td>\n",
       "      <td>stanley street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec-1016-org</th>\n",
       "      <td>courtney</td>\n",
       "      <td>painter</td>\n",
       "      <td>19161214</td>\n",
       "      <td>richlands</td>\n",
       "      <td>txs</td>\n",
       "      <td>pinkerton circuit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             given_name  surname  date_of_birth         suburb state  \\\n",
       "rec_id                                                                 \n",
       "rec-1070-org   michaela   neuman       19151111  winston hills   cal   \n",
       "rec-1016-org   courtney  painter       19161214      richlands   txs   \n",
       "\n",
       "                      address_1  \n",
       "rec_id                           \n",
       "rec-1070-org     stanley street  \n",
       "rec-1016-org  pinkerton circuit  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_A = pd.read_csv('census_A.csv', index_col=0)\n",
    "census_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "50f8be37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>suburb</th>\n",
       "      <th>state</th>\n",
       "      <th>address_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rec-561-dup-0</th>\n",
       "      <td>elton</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19651013</td>\n",
       "      <td>windermere</td>\n",
       "      <td>ny</td>\n",
       "      <td>light setreet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec-2642-dup-0</th>\n",
       "      <td>mitchell</td>\n",
       "      <td>maxon</td>\n",
       "      <td>19390212</td>\n",
       "      <td>north ryde</td>\n",
       "      <td>cal</td>\n",
       "      <td>edkins street</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               given_name surname  date_of_birth      suburb state  \\\n",
       "rec_id                                                               \n",
       "rec-561-dup-0       elton     NaN       19651013  windermere    ny   \n",
       "rec-2642-dup-0   mitchell   maxon       19390212  north ryde   cal   \n",
       "\n",
       "                    address_1  \n",
       "rec_id                         \n",
       "rec-561-dup-0   light setreet  \n",
       "rec-2642-dup-0  edkins street  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_B = pd.read_csv('census_B.csv', index_col=0)\n",
    "census_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f4e68b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('rec-1070-org', 'rec-2642-dup-0')],\n",
       "           names=['rec_id_1', 'rec_id_2'])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generating pairs\n",
    "\n",
    "import recordlinkage\n",
    "\n",
    "#create indexing object\n",
    "indexer = recordlinkage.Index()\n",
    "\n",
    "#generate pairs blocked on state\n",
    "indexer.block('state')\n",
    "pairs = indexer.index(census_A, census_B)\n",
    "pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "94b7010e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>state</th>\n",
       "      <th>surname</th>\n",
       "      <th>address_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec_id_1</th>\n",
       "      <th>rec_id_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rec-1070-org</th>\n",
       "      <th>rec-2642-dup-0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             date_of_birth  state  surname  address_1\n",
       "rec_id_1     rec_id_2                                                \n",
       "rec-1070-org rec-2642-dup-0              0      1      0.0        0.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comparing the dataframes\n",
    "#create a compare object\n",
    "\n",
    "compare_cl = recordlinkage.Compare()\n",
    "\n",
    "#find exact matches for pairs of date_of_birth and state\n",
    "compare_cl.exact('date_of_birth', 'date_of_birth', label='date_of_birth')\n",
    "compare_cl.exact('state', 'state', label='state')\n",
    "\n",
    "#find similar matches for pairs of surname and address_1 using string similarity\n",
    "compare_cl.string('surname', 'surname', threshold=0.85, label='surname')\n",
    "compare_cl.string('address_1', 'address_1', threshold=0.85, label='address_1')\n",
    "\n",
    "#find matches\n",
    "potential_matches = compare_cl.compute(pairs, census_A, census_B)\n",
    "potential_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3f948d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>state</th>\n",
       "      <th>surname</th>\n",
       "      <th>address_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rec_id_1</th>\n",
       "      <th>rec_id_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date_of_birth, state, surname, address_1]\n",
       "Index: []"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#isolate matches with matching values for 2 or more columns\n",
    "matches = potential_matches[potential_matches.sum(axis=1) >= 2]\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b4619e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['rec-561-dup-0', 'rec-2642-dup-0'], dtype='object', name='rec_id')\n"
     ]
    }
   ],
   "source": [
    "#linking dataframes\n",
    "\n",
    "#get indices\n",
    "matches.index\n",
    "\n",
    "#get indices from census_B only\n",
    "duplicate_rows = matches.index.get_level_values(1)\n",
    "print(census_B.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "99ba6593",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "expected pandas.MultiIndex with record pair indices as first argument",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-89-11de698d61ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m#generate potential matches\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mpotential_matches1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompare_cl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_census\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcensus_A\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcensus_B\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m#isolate matches with matching values for 3 or more columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\recordlinkage\\base.py\u001b[0m in \u001b[0;36mcompute\u001b[1;34m(self, pairs, x, x_link)\u001b[0m\n\u001b[0;32m    851\u001b[0m         \"\"\"\n\u001b[0;32m    852\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpairs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 853\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m    854\u001b[0m                 \u001b[1;34m\"expected pandas.MultiIndex with record pair indices \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    855\u001b[0m                 \u001b[1;34m\"as first argument\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: expected pandas.MultiIndex with record pair indices as first argument"
     ]
    }
   ],
   "source": [
    "#find duplicates in census_B\n",
    "census_B_duplicates = census_B[census_B.index.isin(duplicate_rows)]\n",
    "\n",
    "#finding new rows in census_B\n",
    "census_B_new = census_B[~census_B.index.isin(duplicate_rows)]\n",
    "\n",
    "#link the dataframes\n",
    "full_census = census_A.append(census_B_new)\n",
    "\n",
    "#generate potential matches\n",
    "potential_matches1 = compare_cl.compute(full_census, census_A, census_B)\n",
    "\n",
    "#isolate matches with matching values for 3 or more columns\n",
    "matches = potential_matches[potential_matches.sum(axis=1) >= 2]\n",
    "\n",
    "#get index for matching census_B only\n",
    "duplicate_rows = matches.index.get_level_values(1)\n",
    "\n",
    "#finding new rows in census_B\n",
    "census_B_new = census_B[~census_B.index.isin(duplicate_rows)]\n",
    "\n",
    "#link/append the dataframes\n",
    "full_census = census_A.append(census_B_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02f78b8",
   "metadata": {},
   "source": [
    "# Data Cleaning Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2486b1e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['los angeles' 'studio city' 'hollywood' 'beverly hills' 'santa monica'\n",
      " 'new york' 'brooklyn' 'las vegas' 'atlanta' 'san francisco'\n",
      " 'pacific palisades' 'toluca lake' 'sherman oaks' 'los feliz'] \n",
      "\n",
      "['american' 'asian' 'italian' 'coffeebar' 'mexican' 'southwestern'\n",
      " 'steakhouses' 'southern' 'cajun']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>addr</th>\n",
       "      <th>city</th>\n",
       "      <th>phone</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arnie morton's of chicago</td>\n",
       "      <td>435 s. la cienega blv .</td>\n",
       "      <td>los angeles</td>\n",
       "      <td>3102461501</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>art's delicatessen</td>\n",
       "      <td>12224 ventura blvd.</td>\n",
       "      <td>studio city</td>\n",
       "      <td>8187621221</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campanile</td>\n",
       "      <td>624 s. la brea ave.</td>\n",
       "      <td>los angeles</td>\n",
       "      <td>2139381447</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fenix</td>\n",
       "      <td>8358 sunset blvd. west</td>\n",
       "      <td>hollywood</td>\n",
       "      <td>2138486677</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grill on the alley</td>\n",
       "      <td>9560 dayton way</td>\n",
       "      <td>los angeles</td>\n",
       "      <td>3102760615</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>vivande porta via</td>\n",
       "      <td>2125 fillmore st.</td>\n",
       "      <td>san francisco</td>\n",
       "      <td>4153464430</td>\n",
       "      <td>italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>vivande ristorante</td>\n",
       "      <td>670 golden gate ave.</td>\n",
       "      <td>san francisco</td>\n",
       "      <td>4156739245</td>\n",
       "      <td>italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>world wrapps</td>\n",
       "      <td>2257 chestnut st.</td>\n",
       "      <td>san francisco</td>\n",
       "      <td>4155639727</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>wu kong</td>\n",
       "      <td>101 spear st.</td>\n",
       "      <td>san francisco</td>\n",
       "      <td>4159579300</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>yank sing</td>\n",
       "      <td>427 battery st.</td>\n",
       "      <td>san francisco</td>\n",
       "      <td>4155414949</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name                       addr           city  \\\n",
       "0    arnie morton's of chicago   435 s. la cienega blv .     los angeles   \n",
       "1           art's delicatessen       12224 ventura blvd.     studio city   \n",
       "2                    campanile       624 s. la brea ave.     los angeles   \n",
       "3                        fenix    8358 sunset blvd. west       hollywood   \n",
       "4           grill on the alley           9560 dayton way     los angeles   \n",
       "..                         ...                        ...            ...   \n",
       "331          vivande porta via         2125 fillmore st.   san francisco   \n",
       "332         vivande ristorante      670 golden gate ave.   san francisco   \n",
       "333               world wrapps         2257 chestnut st.   san francisco   \n",
       "334                    wu kong             101 spear st.   san francisco   \n",
       "335                  yank sing           427 battery st.   san francisco   \n",
       "\n",
       "          phone      type  \n",
       "0    3102461501  american  \n",
       "1    8187621221  american  \n",
       "2    2139381447  american  \n",
       "3    2138486677  american  \n",
       "4    3102760615  american  \n",
       "..          ...       ...  \n",
       "331  4153464430   italian  \n",
       "332  4156739245   italian  \n",
       "333  4155639727  american  \n",
       "334  4159579300     asian  \n",
       "335  4155414949     asian  \n",
       "\n",
       "[336 rows x 5 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants = pd.read_csv('restaurants_L2.csv', index_col=0)\n",
    "print(restaurants.city.unique(), '\\n')\n",
    "print(restaurants.type.unique())\n",
    "restaurants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4f663991",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['la' 'hollywood' 'pasadena' 'los angeles' 'new york' 'studio city'\n",
      " 'venice' 'santa monica' 'mar vista' 'beverly hills' 'w. hollywood'\n",
      " 'encino' 'st. boyle hts .' 'westlake village' 'westwood' 'west la'\n",
      " 'chinatown' 'monterey park' 'rancho park' 'redondo beach' 'long beach'\n",
      " 'marina del rey' 'culver city' 'burbank' 'century city' 'malibu'\n",
      " 'seal beach' 'northridge' 'st. hermosa beach'] \n",
      "\n",
      "['american' 'californian' 'japanese' 'cajun/creole' 'hot dogs' 'diners'\n",
      " 'delis' 'hamburgers' 'seafood' 'italian' 'coffee shops' 'russian'\n",
      " 'steakhouses' 'mexican/tex-mex' 'noodle shops' 'mexican' 'middle eastern'\n",
      " 'asian' 'vietnamese' 'health food' 'american ( new )' 'pacific new wave'\n",
      " 'indonesian' 'eclectic' 'chicken' 'fast food' 'southern/soul' 'coffeebar'\n",
      " 'continental' 'french ( new )' 'desserts' 'chinese' 'pizza']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>addr</th>\n",
       "      <th>city</th>\n",
       "      <th>phone</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kokomo</td>\n",
       "      <td>6333 w. third st.</td>\n",
       "      <td>la</td>\n",
       "      <td>2139330773</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feenix</td>\n",
       "      <td>8358 sunset blvd. west</td>\n",
       "      <td>hollywood</td>\n",
       "      <td>2138486677</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>parkway</td>\n",
       "      <td>510 s. arroyo pkwy .</td>\n",
       "      <td>pasadena</td>\n",
       "      <td>8187951001</td>\n",
       "      <td>californian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>r-23</td>\n",
       "      <td>923 e. third st.</td>\n",
       "      <td>los angeles</td>\n",
       "      <td>2136877178</td>\n",
       "      <td>japanese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gumbo</td>\n",
       "      <td>6333 w. third st.</td>\n",
       "      <td>la</td>\n",
       "      <td>2139330358</td>\n",
       "      <td>cajun/creole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>feast</td>\n",
       "      <td>1949 westwood blvd.</td>\n",
       "      <td>west la</td>\n",
       "      <td>3104750400</td>\n",
       "      <td>chinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>mulberry</td>\n",
       "      <td>17040 ventura blvd.</td>\n",
       "      <td>encino</td>\n",
       "      <td>8189068881</td>\n",
       "      <td>pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>matsuhissa</td>\n",
       "      <td>129 n. la cienega blvd.</td>\n",
       "      <td>beverly hills</td>\n",
       "      <td>3106599639</td>\n",
       "      <td>asian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>jiraffe</td>\n",
       "      <td>502 santa monica blvd</td>\n",
       "      <td>santa monica</td>\n",
       "      <td>3109176671</td>\n",
       "      <td>californian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>martha's</td>\n",
       "      <td>22nd street grill 25 22nd</td>\n",
       "      <td>st. hermosa beach</td>\n",
       "      <td>3103767786</td>\n",
       "      <td>american</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name                       addr               city       phone  \\\n",
       "0       kokomo          6333 w. third st.                 la  2139330773   \n",
       "1       feenix    8358 sunset blvd. west           hollywood  2138486677   \n",
       "2      parkway       510 s. arroyo pkwy .           pasadena  8187951001   \n",
       "3         r-23           923 e. third st.        los angeles  2136877178   \n",
       "4        gumbo          6333 w. third st.                 la  2139330358   \n",
       "..         ...                        ...                ...         ...   \n",
       "77       feast        1949 westwood blvd.            west la  3104750400   \n",
       "78    mulberry        17040 ventura blvd.             encino  8189068881   \n",
       "79  matsuhissa   129 n. la cienega blvd.       beverly hills  3106599639   \n",
       "80     jiraffe      502 santa monica blvd       santa monica  3109176671   \n",
       "81    martha's  22nd street grill 25 22nd  st. hermosa beach  3103767786   \n",
       "\n",
       "            type  \n",
       "0       american  \n",
       "1       american  \n",
       "2    californian  \n",
       "3       japanese  \n",
       "4   cajun/creole  \n",
       "..           ...  \n",
       "77       chinese  \n",
       "78         pizza  \n",
       "79         asian  \n",
       "80   californian  \n",
       "81      american  \n",
       "\n",
       "[82 rows x 5 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restaurants_dirty = pd.read_csv('restaurants_L2_dirty.csv', index_col=0)\n",
    "print(restaurants_dirty.city.unique(), '\\n')\n",
    "print(restaurants_dirty.type.unique())\n",
    "restaurants_dirty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2734520f",
   "metadata": {},
   "source": [
    "Observation between restaurants and restaurant_dirty shows that restaurant_dirty has columns with inconsistent values which are city and type columns. \n",
    "\n",
    "The values needs to be cleaned to match that of the restaurant dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4170a744",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9a5c05",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "ae5fbfb9",
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   "cell_type": "code",
   "execution_count": null,
   "id": "1166ba4f",
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "750dcbeb",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85761b16",
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   "cell_type": "code",
   "execution_count": null,
   "id": "97e1a7d8",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b26f77ab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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